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The forthcoming arrival of automated vehicles (AV) on the roads requires the re-evaluation or even adaptation of existing infrastructures as they are currently designed on the basis of human perception. Indeed, advanced driver-assistance systems (ADAS) do not necessarily have the same needs as drivers to detect road markings. One of the main challenges related to AV is the optimisation of the vehicle–infrastructure pair in order to guarantee the safety of all users. In this context, we compared the performance of a vehicle equipped with an ADAS machine-vision system with a dynamic retroreflectometer during the daytime on a road section. Our results questioned the reliability of the literature thresholds of the luminance contrast ratio on a dry road under sunny conditions. Despite the presence of old and worn road markings, the ADAS camera was able to detect the edge lines in more than 90% of the cases. The non-detections were not related to the poor condition of the markings but to the environmental conditions or the complexity of the infrastructure.

A safe, comfortable and intelligent Lane Keeping Assistance System (LKAS) is fundamental on the way to realizing full automated driving. In spite of the current high market penetration of LKAS, vehicle manufacturers, suppliers and researchers are still working on improving the system to reach a higher customer acceptance. To achieve this goal more efficiently, a systematic and ideally objective assessment procedure of the system needs to be established. Till date, a thorough review study of the assessment procedures of LKAS does not exist, especially of those which also take the subjective impression of the driver into consideration. The motivation of this paper is to benchmark the state-of-the-art assessment methods of LKAS and identify where problems and research potentials lie. The paper summarizes the relevant characteristics of LKAS based on representative research in the past decades. It also compares the characterization of LKAS with the field of steering feel and vehicle handling in the aspects of procedure, strategy, as well as their advantages and disadvantages. This paper contributes to transferring the know-hows and valuable experiences from other well-developed fields of vehicle dynamics into the rapid-evolving branch of Advanced Driver Assistance System (ADAS), in order to help standardizing its assessment procedure and give insights on system design from a human-centered perspective in the early development phase, thus shortening the ADAS development cycles.

Connected and automated vehicles will characterize the future of the mobility, featured by Smart Roads and Internet of Things technologies. Vehicles will behave as mobile nodes of a network enabling communication between them and with respect to the road infrastructure. In order to provide advanced driver assistance and automated driving along a Smart Road, control strategies need to take into account both parametric variations due to environmental and/or weather conditions (e.g., wind speed, presence of ice, and/or rain), and constraints related to road safety and vehicular traffic. For this reason, advanced adaptive control strategies result particularly suitable to provide a complete control of the vehicle. This article proposes an extension to a previous conference paper, concerning a Processor in the Loop testing of an Adaptive Model Predictive Control for coupled longitudinal/lateral vehicle dynamics, where the ego vehicle was simulated through a bicycle model. In the present work, a low-cost real-time Hardware In the Loop test bench, featured by Raspberry boards, is used to verify the control strategy in a way closer to a real application. A four-wheel vehicle dynamic model is considered to simulate the ego vehicle, then robustness and sensitivity tests with respect to parametric variations related to road friction, wind speed and vehicle mass, and a comparison with an existing adaptive MPC are performed. The achieved results show good robustness properties, safety performances and confirm the real-time execution of the control strategy, paving the way for future developments, where the goal will be the testing in a real road scenario by using an IoT gateway purposefully designed by the NetCom company.

Technical Report, Over 11.2 million Model Year 2017–2021 vehicles were matched to police-reported crashes from 14 states to examine the effectiveness of GM ADAS features. The quasi-induced exposure method was used, with logistic regression used to adjust for 13 covariates. Results indicated fusion/radar Automatic Emergency Braking (AEB), camera AEB, and Forward Collision Alert systems reduced rear-end striking crashes by 49%, 40%, and 14%, respectively. When restricting to crashes with suspected minor or higher injury severities reported, reductions were elevated to 57%, 53%, and 25%, respectively, providing evidence of additional crash mitigation benefits. Similarly, the Lane Keep Assist with Lane Departure Warning system provided 17%, 8%, and 7% reductions in roadway departure, same direction sideswipe, and opposite direction sideswipe crashes, as well as a 22% reduction in injuries reported for roadway departure crashes. The Lane Change Alert with Side Blind Zone Alert system reduced lane change crashes by 15%. The backing crash findings indicated Rear Vision Camera, Rear Park Assist, Rear Cross Traffic Alert, and Reverse Automatic Braking (note more advanced backing features generally include all less advanced backing features) provided 34%, 49%, 62%, and 85% reductions in backing crashes, respectively. Front Pedestrian Braking reduced front pedestrian crashes by 23%. These results provide further evidence of widespread ADAS field benefits, as well as identify opportunities for moving closer toward a zero crashes vision.

Investments in autonomous driving technology have already exceeded $200 billion, yet despite this staggering figure, driving in the rain is still an unmet need. Current self-driving vehicles are unable to drive during unexpected showers and will simply park on the side of the road. Navigating in the rain without proper cleaning technology is very challenging, water droplets will accumulate on lenses and cause distorted visibility. The solution presented in this report, named Dryve, consists of a redesigned camera case that uses a combination of high-speed rotation, hydrophobic coating, and an air blower to keep the camera lens free of droplets. Dryve also consists of a User Interface by which fleet managers of autonomous vehicles can monitor vehicle status and sensor cleaning. By preventing rain droplets from accumulating on the camera lens, Dryve can strongly improve visibility and sensing accuracy. Dryve strives to enable safe and autonomous driving during rainy conditions, hence further encouraging the mass adoption of self-driving technology.

In Europe, 20% of road crashes occur at intersections. In recent years, evolving communication technologies are making V2V and V2I faster and more reliable; with such advancements, these crashes, as well as their economic cost, can be partially reduced. In this work, we concentrate on straight path intersection collisions. Connectivity-based algorithms relying on 5G technology and smart sensors are presented and compared to a commercial radar AEB logic in order to evaluate performances and effectiveness in collision avoidance or mitigation. The aforementioned novel safety systems are tested in a blind intersection and low adherence scenario. The first algorithm proposed is obtained by incorporating connectivity information to the original control scheme, while the second algorithm proposed is a novel control logic fully capable of utilizing also adherence estimation provided by smart sensors. Test results show an improvement in terms of safety for both the architecture and high prospects for future developments.

This paper proposes and implements brightness and color equalization (BCE) methods of images obtained from different cameras on a real advanced driver assistance system (ADAS) platform. In particular, four different methods for BCE that are a combination of a basic linear transformation (BLT) and gamma correction are implemented and tested. In addition to BCE, blending operations based on improved gradual fusion were also carried out to obtain a smooth transition of brightness and color components in the final panoramic image at the overlapping region between the input images. The resulting images were tested subjectively to determine the most preferred method for BCE out of four methods. The developed solution for BCE was implemented on the ADAS ALPHA development board.

The basic tasks of Advanced Driver-Assistance Systems (ADASs) are object detection and tracking. Although these tasks can be performed by processing signals from various in-vehicle sensors, the most used is the image captured by the in-vehicle camera. For an autonomous vehicle to know which action needs to be taken at a given moment, very useful data is about the speed and direction of movement of objects in their surroundings. Once objects are detected, different methods can be used to track them. Although there are various methods for object tracking based on computer vision and machine learning, they are very often not suitable for implementation on the real embedded ADAS platforms that have limited memory and computing resources. Therefore, this paper deals with the motion vectors (MVs) estimation between adjacent frames and their grouping with the goal of identifying and tracking objects around the ego-vehicle, all accompanied by the implementation of the proposed solutions onto a real embedded ADAS platform. Different methods for MV estimation and grouping are examined. Special attention was given to the efficient implementation of each task on the ADAS platform. The proposed solution achieves high performance in terms of accuracy when tested on real-life traffic videos. It is important to note that the solution is able to process 64 frames per second for the input images with a resolution of 1280x720 pixels.

Introduction. Automotive radars are the main tools for providing traffic safety. The development of such radars involve a number of technical difficulties due to the manufacture of high-precision extremely high-frequency (EHF) printed circuit boards. To facilitate the process of creating such devices, the existing algorithms for radar information processing should be debugged using prototypes from manufacturers of mm-band transceivers. However, the parameters of such boards are not known in advance, and the actual operating conditions of the as-produced automotive radars raise new challenges to target tracking algorithms. Therefore, checking the performance of such boards is a relevant research problem.Aim. To evaluate the performance of a millimeter-wave automotive radar prototype and to test target tracking algorithms using this prototype.Materials and methods. An original target tracking method was used, which considers the constraints on the use of additional data sources about the radar carrier movement.Results. An experimental performance evaluation of a 77 GHz automotive radar prototype was carried out. The effectiveness of primary processing for the target class “vehicle” in the millimetre range was checked. Original algorithms for target tracking were proposed and tested.Conclusion. The obtained results show that the prototype board of a transceiver chip is capable of testing tracking algorithms without creating an own automotive radar prototype. Thus, the developmental process can be significantly shortened. Moreover, after creating a hardware solution, the developer obtains a reference device to test and configure an own product without using extremely expensive and rare EHF equipment.

Mental workload (MW) represents the amount of brain resources required to perform concurrent tasks. The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test—DST and Ray Auditory Verbal Learning Test—RAVLT) were administered to participants while driving in a simulated environment. The tests were chosen to investigate the drivers’ response to predefined levels of cognitive load to categorize the classes of MW. Infrared (IR) thermal imaging concurrently with heart rate variability (HRV) were used to obtain features related to the psychophysiology of the subjects, in order to feed machine learning (ML) classifiers. Six categories of models have been compared basing on unimodal IR/unimodal HRV/multimodal IR + HRV features. The best classifier performances were reached by the multimodal IR + HRV features-based classifiers (DST: accuracy = 73.1%, sensitivity = 0.71, specificity = 0.69; RAVLT: accuracy = 75.0%, average sensitivity = 0.75, average specificity = 0.87). The unimodal IR features based classifiers revealed high performances as well (DST: accuracy = 73.1%, sensitivity = 0.73, specificity = 0.73; RAVLT: accuracy = 71.1%, average sensitivity = 0.71, average specificity = 0.85). These results demonstrated the possibility to assess drivers’ MW levels with high accuracy, also using a completely non-contact and non-invasive technique alone, representing a key advancement with respect to the state of the art in traffic accident prevention.

The United States has over three trillion vehicle miles of travel annually on over four million miles of public roadways, which require regular maintenance. To maintain and improve these facilities, agencies often temporarily close lanes, reconfigure lane geometry, or completely close the road depending on the scope of the construction project. Lane widths of less than 11 feet in construction zones can impact highway capacity and crash rates. Crash data can be used to identify locations where the road geometry could be improved. However, this is a manual process that does not scale well. This paper describes findings for using data from onboard sensors in production vehicles for measuring lane widths. Over 200 miles of roadway on US-52, US-41, and I-65 in Indiana were measured using vehicle sensor data and compared with mobile LiDAR point clouds as ground truth and had a root mean square error of approximately 0.24 feet. The novelty of these results is that vehicle sensors can identify when work zones use lane widths substantially narrower than the 11 foot standard at a network level and can be used to aid in the inspection and verification of construction specification conformity. This information would contribute to the construction inspection performed by agencies in a safer, more efficient way.

Many tasks that until recently could only be performed by humans are now entrusted to machines. Automation plays an increasingly important role in many areas of life, and its special role is observed in broadly understood transport, affecting the mobility, behaviour, and safety of drivers. Faced with the threat of a large number of accidents, deaths, and injuries, vehicles equipped with driver-assistance systems, the so-called ADAS (advanced driver-assistance system), are a significant support for the driver in reacting to the unexpected situations. The studies conducted so far indicate that the experience of using these systems may increase not only the comfort but most of all the safety of road users. In Poland, such analyses are still a challenge. The aim of the study designed by scientists from the Motor Transport Institute was to analyse the impact of the experience in using a vehicle equipped with systems on the speedy adaptation to the use of new driver-assistance systems. The adaptation process for the use of three systems was taken into account: adaptive cruise control, blind spot detection system, and lane keeping assistant. The research was carried out in real traffic. Particular attention was paid to the reactions of people who have no experience with the use of driver-assistance systems in vehicles, compared to those who have used these systems before. Proprietary questionnaires on the drivers’ feelings were used for the analyses. As expected, the analyses showed that experience with driver-assistance systems is an important factor influencing the speed of adaptation and the sense of comfort in using the systems.

Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes in ADAS-Cog scores could help in the timing of therapeutic interventions in dementia and at-risk populations. In the present work, we compared single- and multi-task learning approaches to predict the changes in ADAS-Cog scores based on T1-weighted anatomical magnetic resonance imaging (MRI). In contrast to most machine learning-based methods to predict the changes in ADAS-Cog, we stratified the subjects based on their baseline diagnoses and evaluated the prediction performances in each group. Our experiments indicated a positive relationship between predicted and observed ADAS-Cog score changes in each diagnostic group suggesting that T1-weighted MRI has predictive value for evaluating cognitive decline in the whole AD continuum. We further studied whether correction of the differences in magnetic field strength of MRI would improve ADAS-Cog score prediction. The partial least square-based domain adaptation improved slightly prediction performance, but the improvement was marginal. In sum, this study demonstrated that ADAS-Cog change can be, to some extent, predicted based on anatomical MRI. Based on this study, the recommended method for learning the predictive models is a single-task regularized linear regression owing to its simplicity and good performance. It appears important to combine the training data across all subject groups for the most effective predictive models. publishedVersion

Higher levels of autonomous driving are bringing sophisticated requirements and unpredicted challenges. In order to solve these problems, the set of functionalities in modern vehicles is growing in terms of algorithmic complexity and required hardware. The risk of testing implemented solutions in real world is high, expensive and time consuming. This is the reason for virtual automotive simulation tools for testing are heavily acclaimed. Original Equipment Manufacturers (OEMs) use these tools to create closed sense, compute, act loop to have realistic testing scenarios. Production software is tested against simulated sensing data. Based on these inputs a set of actions is produced and simulated which generates consequences that are evaluated. This creates a possibility for OEMs to minimize design errors and optimize costs of the vehicle production before any physical prototypes are produced. This paper presents the development of simple C++/Python perception applications that can be used in driver assistance functionalities. Using ROS as a prototyping platform these applications are validated and tested with "Software-In-the Loop" (SIL) method. CARLA simulator is used as a generator for input data and output commands of the autonomous platform are executed as simulated actions within simulator. Validation is done by connecting Autoware autonomous platform with CARLA simulator in order to test against various scenes in which applications are applicable. Vision based lane detection, which is one of the prototypes, is also tested in a real world scenario to demonstrate the applicability of algorithms developed with simulators to real-time processing

The advent of machine learning (ML) methods for the industry has opened new possibilities in the automotive domain, especially for Advanced Driver Assistance Systems (ADAS). These methods mainly focus on specific problems ranging from traffic sign and light recognition to pedestrian detection. In most cases, the computational resources and power budget found in ADAS systems are constrained while most machine learning methods are computationally intensive. The usual solution consists in adapting the ML models to comply with the memory and real-time (RT) requirements for inference. Some models are easily adapted to resource-constrained hardware, such as Support Vector Machines, while others, like Neural Networks, need more complex processes to fit into the desired hardware. The ADAS hardware (HW platforms) are diverse, from complex MPSoC CPUs down to classical MCUs, DPSs and application-specific FPGAs and ASICs or specific GPU platforms (such as the NVIDIA families Tegra or Jetson). Therefore, there is a tradeoff between the complexity of the ML model implemented and the selected platform that impacts the performance metrics: function results, energy consumption and speed (latency and throughput). In this paper, a survey in the form of systematic review is conducted to analyze the scope of the published research works that embed ML models into resource-constrained implementations for ADAS applications and what are the achievements regarding the ML performance, energy and speed trade-off.

Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances ( i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.

The goal of this research is the development of a driver assistant feature, which can warn the driver in case a pedestrian is in a potential risk due to sudden intention to cross the road. The process of crossing pedestrian is defined as the changing of pedestrian orientation on the curb toward the road. We built a Convolutional Neural Network (CNN) model combined with depth sensing camera to estimate the pedestrian orientation and distance from the vehicle. The model detects the higher human body keypoints in 2D space while the depth info make it possible to translate the points into a 3D space. These info are tracked per pedestrian and any change in the pedestrian moving pattern toward the road is translated to a warning for the driver. The CNN model is end-end trained using different datasets presenting pedestrian in different configurations and scenes.

Pedestrian detection has never been an easy task for computer vision and the automotive industry. Systems like the advanced driver-assistance system (ADAS) highly rely on far-infrared (FIR) data captured to detect pedestrians at nighttime. The recent development of deep learning-based detectors has proven the excellent results of pedestrian detection in perfect weather conditions. However, it is still unknown what the performance in adverse weather conditions is. In this paper, we introduce a 16-bit thermal data dataset called ZUT (Zachodniopomorski Uniwersytet Technologiczny) as having the widest variety of fine-grained annotated images captured in the four biggest European Union countries captured during severe weather conditions. We also provide a synchronized Controller Area Network (CAN bus) data, including driving speed, brake pedal status, and outside temperature for future ADAS system development. Furthermore, we have tested and provided 16-bit depth modifications for the YOLOv3 deep neural network (DNN) based detector, reaching a mean Average Precision (mAP) up to 89.1%. The ZUT dataset is published and publicly available at IEEE Dataport and Github.

Tesla Deaths is a record of Tesla accidents that involved a driver, occupant, cyclist, motorcyclist, or pedestrian death. We record information about Tesla fatalities that have been reported and as much related crash data as possible such as location of crash, names of deceased. This dataset also tallies claimed and confirmed Tesla autopilot crashes, that is instances when Autopilot was activated during a Tesla crash that resulted in death. Latest version of dataset at https://www.tesladeaths.com. CSV version available at https://docs.google.com/spreadsheets/d/e/2PACX-1vQs5yOJJxoKKV3Ol9n0X5LSfnNFyE4verpUBdJOQ2BGUnl54IxwTexaZO69D7rT0VHM5JCxIuwijPIm/pub?output=csv

Shared control techniques for Automated Driving require high-quality equipment to step up from hardware-in-the-loop simulations to real applications. Nevertheless, not every car used nowadays for Automated Driving research has a suitable steering system. This project develops a fast, reliable, and efficient two-way low-level controller of a power steering, focusing on its application in torque and position control. Hence, following this methodology, not only shared control techniques are enabled by the torque control, but also fully automated vehicles can benefit from the position control. Las técnicas de control compartido para la conducción automatizada requieren equipos de alta calidad para pasar de simulación hardware-in-the-loop a aplicaciones reales. Sin embargo, no todos los coches utilizados hoy en día para la investigación de la conducción automatizada cuentan con un sistema de dirección adecuado. Este proyecto desarrolla controladores de bajo nivel, rápidos, fiables y eficientes de par y posición para una dirección asistida. De este modo y, siguiendo esta metodología, el control de par no sólo habilita las técnicas de control compartido, sino que también los vehículos totalmente automatizados pueden beneficiarse gracias al control de posición. Gidatze automatizaturako kontrol partekatuko teknikek kalitate handiko ekipamendua behar dute hardware-in-the-loop simulaziotik benetako aplikazioetara pasatzeko. Hala ere, gaur egun gidatze automatizatua ikertzeko erabiltzen diren auto guztiek ez dute direkzio-sistema egoki bat. Proiektu honek maila baxuko kontrolatzaileak garatzen ditu, azkarrak, fidagarriak eta pare eta posizio-eraginkorrak, lagunduriko direkzio baterako. Horrela, eta metodologia horri jarraiki, pare-kontrolak kontrol partekatuko teknikak gaitzen ditu. Gainera, automatizatutako ibilgailuek posizio-kontrolaren abantailak ere erabil ditzakete.

Lamentablemente, los siniestros de tráfico siguen siendo una de las primeras causas de mortalidad y lesividad en la población española y se han convertido en una pandemia a nivel mundial. La Organización Mundial de la Salud, calcula que alrededor de 1,35 millones de personas siguen muriendo en el mundo anualmente, a causa de los siniestros viales. Actualmente nos encontramos ante un nuevo Decenio de Acción para la Seguridad Vial 21-30, declarado por la Asamblea General de Naciones Unidas, que solicita continuar trabajando para reducir a la mitad la mortalidad y lesividad derivada de estos siniestros. Números estudios siguen afirmando que el factor humano está directamente relacionado con la siniestralidad vial y que, entre el 70% y el 90% de los siniestros, se explican por diversos errores humanos. Al objeto de reducir la siniestralidad vial y mejorar la movilidad, esta Tesis de Psicología por Compendio de Publicaciones investiga sobre factores emergentes en seguridad vial con efectos determinantes en la población conductora española. Estos factores han sido agrupados en tres ejes temáticos relacionados con la tecnología y la automatización de los vehículos, la salud visual y las conductas infractoras reincidentes, que dieron como resultado cuatro publicaciones científicas. Para el desarrollo de las investigaciones, se elaboraron herramientas específicas de recogida de datos, se realizó investigación documental y de campo, con muestras robustas y representativas a nivel nacional y se analizaron los datos obtenidos en busca de respuestas prácticas a estos factores emergentes. En la parte final de este documento, se encuentran los artículos científicos resultantes y un resumen en castellano de cada uno de ellos.

This work presents the development of an ADAS (advanced driving assistance system) focused on driver drowsiness detection, whose objective is to alert drivers of their drowsy state to avoid road traffic accidents. In a driving environment, it is necessary that fatigue detection is performed in a non-intrusive way, and that the driver is not bothered with alarms when he or she is not drowsy. Our approach to this open problem uses sequences of images that are 60 s long and are recorded in such a way that the subject’s face is visible. To detect whether the driver shows symptoms of drowsiness or not, two alternative solutions are developed, focusing on the minimization of false positives. The first alternative uses a recurrent and convolutional neural network, while the second one uses deep learning techniques to extract numeric features from images, which are introduced into a fuzzy logic-based system afterwards. The accuracy obtained by both systems is similar: around 65% accuracy over training data, and 60% accuracy on test data. However, the fuzzy logic-based system stands out because it avoids raising false alarms and reaches a specificity (proportion of videos in which the driver is not drowsy that are correctly classified) of 93%. Although the obtained results do not achieve very satisfactory rates, the proposals presented in this work are promising and can be considered a solid baseline for future works. This work was supported by the Spanish Government under projects PID2019- 104793RB-C31, TRA2016-78886-C3-1-R, RTI2018-096036-B-C22, PEAVAUTO-CM-UC3M and by the Region of Madrid’s Excellence Program (EPUC3M17).

Technical Report, This effort examined the field impact of the Super Cruise system on lane departure crashes and ���quasi��� system-relevant rear-end striking crashes. Two statistical approaches, binomial exact tests and a quasi-induced exposure logistic regression, were used to explore the limited data available more thoroughly. The Super Cruise-equipped vehicles used in this analysis were Model Year 2018-2020 Cadillac CT6 sedans and Model Year 2021 Cadillac CT4, CT5, and Escalade vehicles. Matched Model Year 2016- 2021 vehicles were used as comparison fleets. These fleets were matched to police-reported crashes from 12 states. In light of the widespread lack of statistical significance across the variety of analysis approaches employed and the large number of statistical tests conducted, there is currently no evidence for a difference in system-relevant crash risk for Super Cruise-equipped vehicles compared to matched highly-ADAS equipped vehicles without Super Cruise. Continuing to revisit this Super Cruise field analysis with additional crash data and miles driven should continue to improve our ability to evaluate the effect of Super Cruise on system-relevant crashes in the future.

In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digital twin (xDT). The xDT model is a high fidelity vehicle dynamics simulator, executable online in the control parameter randomization and learning process. The parameters are adapted to gradually improve control performance and deal with changing real-world environment. In particular, the performance metric is not required to be differentiable nor analytical with respect to the control parameters and system dynamics are not necessary linearized. Eventually, the proposed sim2real framework leverages altogether online high fidelity simulator, data-driven estimations, and simulation based optimization to transfer and adapt efficiently a controller developed in simulation environment to the real platform. Our experiment demonstrates that a high control performance is achieved without tedious time and labor consuming tuning., Comment: This work has been accepted to be presented at the 10th IFAC Symposium: Advances in Automotive Control

Technical Report, Over 10.9 million Model Year 2013���2020 vehicles were matched to police-reported crashes from 14 states to examine the effectiveness of GM ADAS features. The quasi-induced exposure method was used, with logistic regression used to adjust for 13 covariates. Results indicated fusion/radar Automatic Emergency Braking (AEB), camera AEB, and Forward Collision Alert systems reduced rear-end striking crashes by 45%, 40%, and 20%, respectively. When restricting to crashes with suspected minor or higher injury severities reported, reductions were elevated to 58%, 55%, and 25%, respectively, providing evidence of additional crash mitigation benefits. Similarly, the Lane Keep Assist with Lane Departure Warning system provided 17% and 10% reductions in roadway departure and same direction sideswipe crashes, respectively, with corresponding injury reduction benefits elevating to 21% and 16%, respectively. The Lane Change Alert with Side Blind Zone Alert system reduced lane change crashes by 16%. Reverse Automatic Braking (which includes Rear Cross Traffic Alert, Rear Park Assist, and Rear Vision Camera) produced an 83% reduction in backing crashes. Front Pedestrian Braking reduced front pedestrian crashes by 23%. IntelliBeam (auto high beam headlighting) provided a 22% reduction in a combined set of (unlighted) nighttime animal, pedestrian, and bicyclist crashes. These results provide further evidence of widespread ADAS field benefits, as well as identify opportunities for moving closer toward a zero crashes vision.

Driver identification refers to the process whose primary purpose is identifying the person behind the steering wheel using collected information about the driver him/herself. The constant monitoring of drivers through sensors generates great benefits in advanced driver assistance systems (ADAS), to learn more about the behavior of road users. Currently, there are many research works that address the subject in search of creating intelligent models that help to identify vehicle users in an efficient and objective way. However, the different methodologies proposed to create these models are based on data generated from sensors that include different vehicle brands on routes established in real environments, which, although they provide very important information for different purposes, in the case of driver identification, there may be a certain degree of bias due to the different situations in which the route environment may change. The proposed method seeks to intelligently and objectively select the most outstanding statistical features from motor activity generated in the main elements of the vehicle with genetic algorithms for driver identification, this process being newer than those established by the state-of-the-art. The results obtained from the proposal were an accuracy of 90.74% to identify two drivers and 62% for four, using a Random Forest Classifier (RFC). With this, it can be concluded that a comprehensive selection of features can greatly optimize the identification of drivers.

The possibility of using some warnings inside modern vehicles should be an aid to the driving activity. However, the information transferred to users is not always received in the expected way due to the variability and complexity of the road environment. This study, therefore, aims to identify a procedure that allows to ascertain whether drivers receive the data in an appropriate way even during particular manoeuvres, such as passing cyclists on a winding road or, on the contrary, if they represent an unnecessary overload. To answer this question, an experimentation in a simulated environment was set up with which the drivers’ visual behaviour was recorded in presence and absence of a driving aid device (On- Board Unit, OBU). The results show that, in some situation, the information provided by the OBU helps to maintain a more virtuous driving behaviour but, in the most complex ones, drivers acquire information from a smaller number of sources, excluding the aid devices present inside the cockpit. This procedure is useful for ADAS designers in order to refine these instruments but also for road managers who can improve safety by inserting appropriate signs or speed limits.

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver’s health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle’s central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver’s heart condition and vehicular traffic have been found in this analysis.

There is ample research on assistance systems for drivers in conventional and automated vehicles. In the past, those systems were developed to increase safety but also to increase driver comfort. Since many common risks have by now been mitigated through such systems, the research and development focus expanded to also include comfort-related assistance. However, the passenger has rarely been taken into account explicitly, although it has been shown that passenger discomfort is a relevant problem. Therefore, this work investigated the potential of passenger assistance systems to reduce such discomfort. Three different passenger assistant system prototypes were tested in a driving study on public highway with N = 19 participants. The systems provided information about parameters related to the performance of the driver and one additionally provided a communicative means of influence. For two passenger assistant systems, it could be shown that they significantly reduced passenger discomfort in at least a subset of the evaluated situations. The majority of participants rated one or multiple of the assistant systems as more comfortable than a ride without assistance. The system providing information about the attentiveness of the driver was most effective in reducing discomfort and was rated as the most helpful system. The results show that explicitly considering the situation of passengers in the design of assistance systems can positively impact their comfort. This can be achieved using information from common systems targeting driver assistance available to the passenger.

A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network-TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167amp;nbsp;kmh (0.046amp;nbsp;ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863amp;nbsp;kmh (0.24amp;nbsp;ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.

Lane-keeping assistance design for road vehicles is a multi-objective design problem that needs to simultaneously maintain lane tracking, ensure driver comfort, provide vehicle stability, and minimize conflict between the driver and the autonomous controller. In this work, a cooperative control strategy is proposed for lane-keeping keeping by integrating driving monitoring, variable level of assistance allocation, and human-in-the-loop control. In the first stage, a time-varying physical driver loading pattern is identified based on a relationship between lateral acceleration, road curvature, and the measured maximum driver torque. Together with the monitored driver state that indicates driver mental loading, an adaptive driver activity function is then formulated that replicates the levels of assistance required for the driver in the next stage. To smoothly transition authority between various modes (from manual to autonomous and vice versa) based on the generated levels of assistance, a novel higher-order sliding mode controller is proposed and closed-loop stability is established. Further, a novel sharing parameter (which is proportional to the torques coming from the driver and from the autonomous controller) is used to minimize the conflict. Experimental results on the SHERPA high-fidelity vehicle simulator show the real-time implementation feasibility. Extensive experimental results provided on the Satory test track show improvement in cooperative driving quality by 9.4%, reduction in steering workload by 86.13%, and reduced conflict by 65.38% when compared with the existing design (no sharing parameter). These results on the cooperative performance highlight the significance of the proposed controller for various road transportation challenges.

The radiated immunity of passenger cars and commercial vehicles is validated using electromagnetic interference from off-vehicle radiation sources in an anechoic chamber according to the procedure in ISO 11451-2. Common electrical or electronic vehicle systems, including dual flash, monitors, and entertainment systems, must be validated using a standardized immunity evaluation procedure. However, Automated Driver Assistance System (ADAS) functions often lack verification in complex electromagnetic fields, which might create potential risk due to electromagnetic compatibility problems. For example, radar and camera sensors based on autonomous emergency braking (AEB), adaptive cruise control (ACC), or lane departure warning (LDW) functions may be ineffective in the real world if subjected to electromagnetic interference. This paper presents a new methodology to evaluate vehicle immunity performance based on the main ADAS functions. A target simulator and a virtual driving environment are designed to trigger AEB, ACC, and LDW functions in an anechoic chamber. Moreover, the vehicle In-the-Loop (VHIL) conception is also applied by acquiring and analyzing key ADAS-related signals. The reliability of ADAS functions at the whole-vehicle level can be evaluated and improved using standardized external radiation sources.

In recent times, many innocent people are suffering from sudden death for the sake of unwanted road accidents, which also riveting a lot of financial properties. The researchers have deployed advanced driver assistance systems (ADAS) in which a large number of automated features have been incorporated in the modern vehicles to overcome human mortality as well as financial loss, and lane markings detection is one of them. Many computer vision techniques and intricate image processing approaches have been used for detecting the lane markings by utilizing the handcrafted with highly specialized features. However, the systems have become more challenging due to the computational complexity, overfitting, less accuracy, and incapability to cope up with the intricate environmental conditions. Therefore, this research paper proposed a simple encode-decode deep learning model to detect lane markings under the distinct environmental condition with lower computational complexity. The model is based on SegNet architecture for improving the performance of the existing researches, which is trained by the lane marking dataset containing different complex environment conditions like rain, cloud, low light, curve roads. The model has successfully achieved 96.38% accuracy, 0.0311 false positive, 0.0201 false negative, 0.960 F1 score with a loss of only 1.45%, less overfitting and 428 ms per step that outstripped some of the existing researches. It is expected that this research will bring a significant contribution to the field lane marking detection.

Vehicle automation and assistance technologies have been touted as a means by which to reduce traffic collisions by minimizing or eliminating “error-prone” and inefficient human operators. In concept, automation exists on a continuum that includes engaged driving by a human operator augmented by automated support features, vigilant driver monitoring of vehicle behavior with the possibility of driver take-over, to full automation with no active monitoring by a human operator. Moreover, the degree of automation varies by vehicle features (e.g., lane centering, emergency braking, adaptive cruise control, parking), by setting, meaning that automated features may or may not be available depending on specific attributes of the traffic environment (e.g., traffic volume, road geometry, etc), and by implementation (e.g., haptic vs auditory warnings). Thus, these automotive “transportation tools” are highly heterogeneous and pose unique challenges and opportunities for driver training. In this paper, we report the results of an experimental study (n=36) to determine if enhanced vehicle feedback influences driver trust, effort, frustration, and performance (indexed by reaction time) in a virtual driving environment. Results are contextualized in the extant literature on learning to operate motor vehicles and outline key research questions essential for understanding the processes by which skilled performance develops with respect to a real-world practical tool: the increasingly automated automobile.

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.

Indiana University-Purdue University Indianapolis (IUPUI), An autonomous car is a self-driving vehicle, that operates without human intervention and has the capability of sensing the environment around it. To achieve this, the autonomous vehicle mostly depends on multiple Sensors, Actuators, Machine learning, complex algorithms and processors for software execution. Developed Software, at that point, processes all the information obtained from sensors, plans the path, and the instructions are passed to the vehicle���s actuators, which are capable of controlling acceleration, steering, and brake systems. The rules that are hard-coded, algorithms for detection of object and obstacle avoidance, and predictive modelling control algorithms assist the software with observing traffic guidelines and navigate the vehicle accordingly. Free driving is anything but a simple assignment, and to make independent driving game plans is an extraordinarily critical capacity in the current programming planning field. Engineers and Researchers have been keeping huge endeavors to develop safe and precise algorithms to be incorporated in autonomous vehicles. ROS is a flexible and perfect middle ware tool for robotic applications. ROS offers the necessary tools to effortlessly get the sensors information, process that information, and produce a suitable response to actuators of the vehicle. This thesis work plans to exhibit how ROS could be utilized as a middle- ware tool to make the vehicle move autonomously by examining the surroundings and taking decision. The main focus of this thesis is to develop a one-tenth scale of an autonomous Racecar equipped with Jetson Nano as the on-board computer, ROS based software architecture, sensors, and a PWM driver and implement ADAS features such as Emergency Brake system, Lane Detection and Lane change on the autonomous Race car vehicle. At last, by following the strategies introduced in this thesis work, it is possible to build and develop an autonomousvehicle that uses ROS framework.

Proyecto en el que se trabaja la mejora del Sistema Multimedia R-LINK 2 utilizado en los vehículos Renault, de forma que el usuario no vea comprometida su seguridad al volante cuando interactúa con el ordenador a bordo; por lo que se busca el reducir el tiempo de reacción en las acciones que este realiza y que la experiencia de interacción sea óptima. Projecte en el qual es treballa la millora del Sistema Multimèdia R-LINK 2 utilitzat en els vehicles Renault, de manera que l'usuari no vegi compromesa la seva seguretat al volant quan interactua amb l'ordinador a bord; pel que es busca el reduir el temps de reacció en les accions que aquest realitza i que l'experiència d'interacció sigui òptima. A project to improve the R-LINK 2 Multimedia System used in Renault vehicles, so that the user's safety at the wheel is not compromised when interacting with the on-board computer; the aim is to reduce the reaction time in the actions performed and to ensure that the interaction experience is optimal.

El interés actual por trasladar el grueso de la computación de multitud de aplicaciones al borde (Edge Computing), en particular en sistemas basados en Inteligencia Artificial y Machine Learning IA/ML, responde a la necesidad de garantizar la respuesta en tiempo real y la seguridad de los sistemas en caso de fuga de información, requisitos que la computación en la nube (Cloud Computing) no puede satisfacer. Los sistemas ADAS (Advanced Driving Assistance Systems) son un campo de aplicación creciente que responde a esta demanda, con requisitos muy exigentes en términos de coste, consumo, velocidad y seguridad, que un sistema en el borde puede satisfacer. Este trabajo forma parte de un proyecto que investiga la aplicabilidad de las imágenes hiper-espectrales para mejorar las prestaciones y la robustez de los ADAS. En concreto, se estudian diferentes opciones de IPs de microprocesadores de licencia abierta para el diseño de SoCs (System-On-Chip) sobre FPGAs de gama baja/media con aplicación al procesamiento de imágenes hiper-espectrales. Como alternativa a los microprocesadores "Softcore", también se ha estudiado un SoC basado en un microprocesador "Hardcore", en concreto un PSoC de Xilinx de la Familia Zynq-7000. En primer lugar, el trabajo caracteriza el rendimiento de los microprocesadores analizados en una tarea de preprocesamiento de imágenes requerida en cámaras hiper-espectrales de 25 bandas de tipo snapshot con filtro en mosaico. En segundo lugar, se utiliza una arquitectura de SoC genérica con DMAs para analizar la forma óptima de transferir información entre los diferentes IP Cores que conforman el diseño y la memoria externa. Durante el desarrollo del trabajo también se han explorado varias herramientas de diseño de código abierto utilizadas en la síntesis/simulación/verificación de diseños HDL como alternativa a herramientas comerciales de mayor implantación. Egun, aplikazio ugarien konputazio-zamak ertzera eramateko (Edge Computing) interes handia dago, bereziki Adimen Artifizialean eta Machine Learning-ean (AA/ML) oinarritutako sistemetan. Joera horren arrazoi nagusia informazio-ihesak ekiditea, segurtasuna bermatzea eta sistemen denbora errealeko erantzuna bermatzea dira, hodeian konputatzeak (Cloud Computing) ezin bai ditu baldintza horiek bete. Gidatzen laguntzeko sistema aurreratuak edo ADAS (Advanced Driving Assistance Systems) gero eta aplikazio-eremu garrantzitsuagoa da, eta eskari horiei erantzun behar dioten sistemak garatzea eskatzen du, oso eskakizun zorrotzak bai dituzte kostuari, kontsumoari, abiadurari eta segurtasunari dagokienez. Lan hau ADASen prestazioak eta sendotasuna hobetzeko irudi hiper-espektralen aplikagarritasuna ikertzen duen proiektu baten parte da. Zehazki, gama baxu/ertaineko FPGAtan irudi hiper-espektralak prozesatzeko SoCak (System–On-Chip) gauzatzeko lizentzia irekiko mikroprozesadoreen IPen hainbat aukera aztertzen dira. Nukleo “bigun” edo softcore mikroprozesadoreen alternatiba gisa, nukleo zurrun edo hardcore mikroprozesadorean oinarritutako SoC bat ere aztertu da, zehazki Zynq-7000 familiako Xilinxen PSoC bat. Lehenik eta behin, lana honek analizatutako mikroprozesadoreen errendimendua ezaugarritzen du mosaiko iragazkia duten snapshot motako 25 bandako kamera hiper-espektraletan beharrezkoa den irudien aurre-prozesamenduko ataza batean. Bigarrenik, DMAak dituen SoC generikoko arkitektura bat erabili egin da kanpoko memoria eta sistema osatzen duten IP nukleo desberdinen artean informazioa transferitzeko modurik egokiena aztertzeko. Lana garatu bitartean, HDL diseinuen sintesian/simulazioan/egiaztapenean erabiltzen diren kode irekiko diseinu-tresnak ere aztertu dira, ezarpen handieneko tresna komertzialen alternatiba gisa. The current interest in moving the bulk of the computing of a multitude of applications to the edge (Edge Computing), particularly in systems based on Artificial Intelligence and Machine Learning (AI/ML), responds to the need to ensure real-time response and security of systems in case of information leakage, requirements that Cloud Computing cannot satisfy. ADAS systems are a growing field of application that responds to this demand, with very demanding requirements in terms of cost, consumption, speed and security, which a system at the edge can satisfy. This work is part of a research proyect about the applicability of hyperspectral imaging to improve the performance and robustness of ADAS. Specifically, different options of open license microprocessor IPs are studied for the design of SoCs (System-On-Chip) on low/mid-range FPGAs with application to hyperspectral image processing.As an alternative to "Softcore" microprocessors, a SoC based on a "Hardcore" microprocessor, in particular a Xilinx PSoC of the Zynq-7000 Family, has also been studied. First, the work characterizes the performance of the analyzed microprocessors on an image preprocessing task required on 25-band snapshot-type hyperspectral cameras with mosaic filtering. Secondly, a generic SoC architecture with DMAs is used to analyze the optimal way to transfer information between the different IP Cores that make up the design and the external memory. During the development of the work, several open source design tools used in the synthesis/simulation/verification of HDL designs have also been explored as an alternative to more widely deployed commercial tools.

In this paper, we suggest a novel Ecological Adaptive Cruise Control (Eco-ACC) system for an autonomous electric vehicle able to drive its motion while minimizing as much as possible its energy consumption. To this aim, we consider a Nonlinear Model Predictive Control (NMPC) method enhanced with an off-line Computational-intelligence (CI)-based optimization algorithm, i,e. the Improved-Grey Wolf Optimizer (I-GWO). Specifically, since the control performances strongly depend on the proper selection of the NMPC cost function, we propose the I-GWO algorithm to help the control designer find the sub-optimal weighting factors of the dynamic cost function optimized via the NMPC. An extensive numerical analysis involving realistic vehicle dynamics and a real-life Italian road network route confirm the effectiveness of the proposed approach in guaranteeing the ACC control objectives while ensuring energy saving.

Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role. During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information.

Advanced driver-assistance system(s) (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. Wired solutions of vision sensors in ADAS already exist, but are costly and do not cater for low-end vehicles. General ADAS use wired harnessing for communication; this approach eliminates the need for cable harnessing and, therefore, the practicality of a novel wireless ADAS solution was tested. A low-cost alternative is proposed that extends a smartphone’s sensor perception, using a camera-based wireless sensor network. This paper presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. The proposed system makes ADAS features accessible to cheaper vehicles and investigates the possibility of using a wireless network to communicate ADAS information in a intra-vehicle environment. Other ADAS smartphone approaches make use of a smartphone’s onboard sensors; however, this paper shows the application of essential ADAS features developed on the smartphone’s ADAS application, carrying out both lane detection and collision detection on a vehicle by using wireless sensor data. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. The network’s performance was analysed to ensure that the network could carry out detection in real-time. A low-cost CMOS camera sensor network with a smartphone found an application, using Wi-Fi Direct, to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system.

Automotive companies have been developing vehicles with advanced features that aid in various driving tasks. These features are aimed at enhancing safety by either warning the drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane or a constant headway. They are part of vehicles with driver assistance technology and are vital for the successful deployment of connected and automated vehicles in the future. However, drivers’ behavioral response when driving vehicles with such advanced driver assistance systems (ADAS) compared to vehicles without ADAS may vary and is meagerly explored. This research evaluates drivers’ behavioral response to scenarios when driving vehicles with ADAS like lane departure warning (LDW), blind spot warning (BSW), and over speed warning (OSW) compared to vehicles without ADAS. Rural, urban and freeway driving scenarios were developed in a driver simulator and tested on 43 participants aged sixteen years to sixty-five years. The results show that ADAS influence driving behavior by making drivers less aggressive and harmonizing the driving environment. The influence of ADAS on the driver behavior was different in rural, urban, and freeway driving scenarios. The drivers’ behavioral response to scenarios varied with the lighting and weather condition as well as with the age, gender, and ethnicity of the participant. While ADAS help by reducing lane departures or speeding and enhance safety, an indirect influence on braking, turning, and car-following were also observed. The findings help assess driver behavior when driving vehicles with advanced features and build better systems.

Safety-critical advanced driver-assistance systems (ADAS) are expected to benefit from cellular vehicle-to-everything communication, providing both short-range and long-range wireless connectivity between vehicles and road/network infrastructure equipment. However, upon switching between adjacent coverage areas belonging to different network operators, the wireless connectivity may be discontinued. This issue prevents network service continuity and causes interruption of ADAS service availability. In this paper, we aim at assessing the average duration of service interruption due to such network reselection procedures. We provide performance evaluation results highlighting that there is a wide gap between the best case scenario and the worst case scenario. Moreover, we propose a simple heuristic to limit the downside of the lack of cooperation between network operators; our heuristic algorithm, which does not require a tight integration between the operators, performs much better than the worst case and it is fairly comparable with the best case scenario. Nevertheless the present work highlights the importance of a tighter integration between network operators to reduce the delay to the minimum.

Objective To investigate how well gaze behavior can indicate driver awareness of individual road users when related to the vehicle’s road scene perception. Background An appropriate method is required to identify how driver gaze reveals awareness of other road users. Method We developed a recognition-based method for labeling of driver situation awareness (SA) in a vehicle with road-scene perception and eye tracking. Thirteen drivers performed 91 left turns on complex urban intersections and identified images of encountered road users among distractor images. Results Drivers fixated within 2° for 72.8% of relevant and 27.8% of irrelevant road users and were able to recognize 36.1% of the relevant and 19.4% of irrelevant road users one min after leaving the intersection. Gaze behavior could predict road user relevance but not the outcome of the recognition task. Unexpectedly, 18% of road users observed beyond 10° were recognized. Conclusions Despite suboptimal psychometric properties leading to low recognition rates, our recognition task could identify awareness of individual road users during left turn maneuvers. Perception occurred at gaze angles well beyond 2°, which means that fixation locations are insufficient for awareness monitoring. Application Findings can be used in driver attention and awareness modelling, and design of gaze-based driver support systems.

Modern cars use advanced electronic systems that help the driver with the driving process - so-called Advanced Driver-Assistance Systems (ADAS). ADAS systems are used to automate, customize and improve systems within a vehicle for greater safety and better driving experience. Since ADAS systems as such can have a significant impact on the driving process, the vehicle and the driver, they must be thoroughly tested and developed within many industry standards. The key factor in their work is communication between individual system components. This standardized communication is necessary to test, which is usually performed by developing AUTomotive Open System Architecture (AUTOSAR) communication tests. Since ADAS testing can be quite a complex and time-consuming process, automated testing is performed in an appropriate testing environment. In this paper, existing ADAS environment testing systems is presented, which generates a test environment for the simulation of communication in the middle layer (Middleware) of AUTOSAR architecture. Test Environment Generator (TEG) is a Python program for processing ARXML test files based on which it generates a test environment model in the form of separate components in the C programming language. The program consists of input data parsing, parsed data storing and components generation that build the test environment. Based on the detected disadvantages of the existing TEG, several modifications are proposed in order to accelerate its execution time and to introduce more robust and stable data storage methods in database form.

Many new technologies for ADAS are emerging in recent years. In this scenario, LiDARs are becoming one of the pillars for environmental sensing needed in ADAS.Driven by the automotive industry, nowadays many new manufacturers are continuously putting new LiDAR systems on the market, thus increasing LiDARs availability and concomitantly reducing prices. As a result, LiDARs are today finding many new applications in fields such as agriculture and industrial automation.In this paper, we describe and discuss a measurement procedure for the analysis of the performances of LiDARs and, we report the results obtained from the characterization of one the most widespread LiDAR systems — namely VLP 16 by Velodyne. In particular, we compare the estimates provided by the LiDAR with the estimates provided by an absolute interferometer. To the best of our knowledge, these are the first published data obtained comparing a LiDAR and an absolute interferometer.The proposed tests investigated both warm-up and stability and, errors in the measured coordinates.The obtained results indicate a warm-up times of about 42 min and errors of few millimeters over a measuring interval of about (2, 21) m.

A new strategy for millimeter-wave (mm-Wave) circuit and system synthesis, where the accuracy of electromagnetic simulations can be achieved in optimization-based design methodologies without sacrificing efficiency, is presented and tested within a real industrial project. This is done by properly partitioning the system, generating libraries of passive devices which are electromagnetically simulated prior to any circuit optimization, generating performance tradeoffs at different hierarchical levels with multiobjective optimization algorithms and hierarchically composing lower level sub-blocks. With this proposed solution, an entire mm-Wave system, from the passive component level up to the system level, has been designed and compared with the results obtained from a conventional design approach, demonstrating the outstanding capabilities of the methodology.