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Enhancing hazmat truck safety through advanced driving assistance systems (ADAS) relies on both system efficacy and driver reactions. This study investigates the driving behaviors of hazmat truck drivers in response to forward collision warnings (FCWs). Traditional warning triggering methods struggle to capture diverse and immediate driver responses; therefore, our research employs a vision‐based framework for driving data extraction and utilizes the K‐means++ clustering method for response‐based classification. Moreover, we propose an enhanced version of the intelligent driver model (IDM) based on the concept of a virtual vehicle to reproduce hazmat truck drivers' differential behaviors during risky car‐following periods, achieving results that depict improved driving simulations. This model is compared with classic benchmarks, including the IDM, optimal velocity model (OVM), and full velocity difference (FVD) model, demonstrating superior performance in terms of traffic stability and safety in extreme scenarios. Our findings highlight that preaction drivers tend to accelerate before receiving warnings, opting to overtake rather than maintain safe distances. In contrast, calm drivers decelerate in anticipation of the warning, showcasing their awareness of maintaining safety. The analysis reveals that aggressive drivers are predominantly in the 41–45 age group, indicating a higher skill level, while calm drivers are more commonly older, reflecting a trend in cautious driving behaviors. Overall, our research contributes to the development of effective ADAS by considering real‐time driver responses and emphasizes the potential of our model to revolutionize commercial ADAS adoption and enhance road safety for hazmat operations. [ABSTRACT FROM AUTHOR]

Advanced Driving Assistance Systems (ADAS), such as collision avoidance systems and adaptive cruise control, are important features of autonomous driving and are gaining importance day by day in terms of increasing road safety. To increase the reliability of the system, virtual simulation environments are used during the design and development stages. This study examines the effect of ADAS features on energy parameters during the driving cycle in a virtual simulation environment. The discussion focuses on the simulation of an electric vehicle and the relationship between energy use and ADAS functions. For ADAS modeling and simulation, a dynamic model was developed in MATLAB and examined throughout the NEDC drive cycle. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an efficient lossy coding procedure specifically tailored for handling video sequences of automotive high-dynamic range (HDR) image sensors in advanced driver-assistance systems (ADASs) for autonomous vehicles. Nowadays, mainly for security reasons, lossless compression is used in the automotive industry. However, it offers very low compression rates. To obtain higher compression rates, we suggest using lossy codecs, especially when testing image processing algorithms in software in-the-loop (SiL) or hardware-in-the-loop (HiL) conditions. Our approach leverages the high-quality VP9 codec, operating in two distinct modes: grayscale image compression for automatic image analysis and color (in RGB format) image compression for manual analysis. In both modes, images are acquired from the automotive-specific RCCC (red, clear, clear, clear) image sensor. The codec is designed to achieve a controlled image quality and state-of-the-art compression ratios while maintaining real-time feasibility. In automotive applications, the inherent data loss poses challenges associated with lossy codecs, particularly in rapidly changing scenes with intricate details. To address this, we propose configuring the lossy codecs in variable bitrate (VBR) mode with a constrained quality (CQ) parameter. By adjusting the quantization parameter, users can tailor the codec behavior to their specific application requirements. In this context, a detailed analysis of the quality of lossy compressed images in terms of the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) metrics is presented. With this analysis, we extracted some codec parameters, which have an important impact on preservation of video quality and compression ratio. The proposed compression settings are very efficient: the compression ratios vary from 51 to 7765 for grayscale image mode and from 4.51 to 602.6 for RGB image mode, depending on the specified output image quality settings. We reached 129 frames per second (fps) for compression and 315 fps for decompression in grayscale mode and 102 fps for compression and 121 fps for decompression in the RGB mode. These make it possible to achieve a much higher compression ratio compared to lossless compression while maintaining control over image quality. [ABSTRACT FROM AUTHOR]

Crashes involving Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) have been increasing in recent years. Understanding the characteristics of these crashes can guide the optimization of driving automation systems and the policies improving the safety of mixed traffic. However, due to the limited available data, the crashes of ADS- and ADAS-controlled vehicles are still under-investigated. Thus, utilizing the latest National Highway Traffic Safety Administration crash reports, our study explores the patterns and contributing factors of ADAS- and ADS-involved crashes. The sequences of events leading to crashes were extracted from the reports and then categorized into five clusters. Next, for incomplete records, a non-parametric imputation method was applied based on Random Forest. Finally, logistic regression models were built to explore the factors associated with the crashes. The results show that the automation level, speed limit, and vehicle speed are predictors of crash patterns. At the same time, the crash pattern, combined with incident time, roadway type, roadway surface, and vehicle model year are associated with crash outcomes (i.e. contact area and injury severity). The results indicate that further improvement of the ADS/ADAS control algorithms and driver education may be needed to improve the safety of mixed traffic. [ABSTRACT FROM AUTHOR]

As many as 4,601 people were injured or killed on the roads in Norway in 2022. This number is too high and highlights the necessity of putting road safety on the agenda. The car industry today is represented by a vast increase in advanced technology for drivers to interact with, and knowing how and when to use this technology is crucial for safe driving. Thus, our research question was: Does the Norwegian driver training industry teach advanced driver assistance technology (ADAS) and touch screens in their driver training? A survey was distributed to 1,058 driving schools in Norway, resulting in 333 responses. The results showed that driving instructors spend considerably more effort on teaching inbuilt driving assist technologies that enhance driving safety than on those that are designed to enhance safety but that, if not understood well, could lead to hazardous outcomes. Suggestions on how to improve driver training and driving instructor education are made. Research and practical implications are presented to make sure that driving education is updated and aligned with technological advancements in the automobile industry and to ensure traffic safety for all road users. [ABSTRACT FROM AUTHOR]

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver's ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology. [ABSTRACT FROM AUTHOR]

Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vehicle vibrations, limited computational resources, data privacy concerns, and the inherent variability in driver behavior. Analyzing driver states using visible spectrum imaging is particularly challenging under low-light conditions, such as at night. Additionally, relying on a single behavioral indicator often fails to provide a comprehensive assessment of the driver's condition. To address these challenges, we propose a system that operates exclusively in the far-infrared spectrum, enabling the detection of critical features such as yawning, head drooping, and head pose estimation regardless of the lighting scenario. It integrates a channel fusion module to assess the driver's state more accurately and is underpinned by our custom-developed and annotated datasets, along with a modified deep neural network designed for facial feature detection in the thermal spectrum. Furthermore, we introduce two fusion modules for synthesizing detection events into a coherent assessment of the driver's state: one based on a simple state machine and another that combines a modality encoder with a large language model. This latter approach allows for the generation of responses to queries beyond the system's explicit training. Experimental evaluations demonstrate the system's high accuracy in detecting and responding to signs of driver fatigue and distraction. [ABSTRACT FROM AUTHOR]

Computer vision has significantly influenced recent advancements in autonomous driving by providing cutting-edge solutions for various challenges, including object detection, semantic segmentation, and comprehensive scene understanding. One specific challenge is ego-vehicle trajectory segmentation, which involves learning the vehicle's path and describing it with a segmentation map. This can play an important role in both autonomous driving and advanced driver assistance systems, as it enhances the accuracy of perceiving and forecasting the vehicle's movements across different driving scenarios. In this work, we propose a deep learning approach for ego-trajectory segmentation that leverages a state-of-the-art segmentation network augmented with guidance cues provided through various merging mechanisms. These mechanisms are designed to direct the vehicle's path as intended, utilizing training data obtained with a self-supervised approach. Our results demonstrate the feasibility of using self-supervised labels for ego-trajectory segmentation and embedding directional intentions within the network's decisions through image and guidance input concatenation, feature concatenation, or cross-attention between pixel features and various types of guidance cues. We also analyze the effectiveness of our approach in constraining the segmentation outputs and prove that our proposed improvements bring major boosts in the segmentation metrics, increasing IoU by more than 12% and 5% compared with our two baseline models. This work paves the way for further exploration into ego-trajectory segmentation methods aimed at better predicting the behavior of autonomous vehicles. [ABSTRACT FROM AUTHOR]

ADAS (Advanced Driver Assistance Systems) are becoming increasingly popular in on-road vehicles due to their safety, productivity, and cost savings. In the same way, off-road vehicles can benefit from ADAS systems to improve the security of drivers and workers in industrial settings. In this work, we study, design, and develop a novel security system to be integrated into industrial vehicles. This system is built to provide one-way Human Computer Interaction, from the computer to the human, so providing, through the interaction with the ADAS system, feedback to drivers about their surroundings, such as nearby workers, and thus helping to avoid collisions and prevent incidents. The study evaluates the quality of different feedback mechanisms, with the goal of designing the ADAS that produces the best User eXperience (UX). These feedback mechanisms are generated by LEDs in different display formats and colors, as well as with haptic feedback. We created a hybrid testbed using a realistic ADAS and a forklift simulator, integrating the system into a physical structure that resembles an industrial vehicle (a forklift) and used a computer-based simulation of a warehouse to gather the information from users. We performed a study with 36 people for the evaluation of the different feedback mechanisms tested, evaluating the results both from an objective point of view based on the results of the simulation, and a subjective point of view through a questionnaire and the stress of the users in each test. [ABSTRACT FROM AUTHOR]

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology.

Abstract In the present study road safety impact analysis for certain advanced driver assistance systems (ADAS) was conducted. Based on a literature review, expert interviews and current adaptations in legislation, the most promising nine ADAS were selected. The impact was analysed based on statistical crash data from Austria. Factors such as infrastructure and weather conditions, market penetration, expected functionality of sensors, user acceptance and risk homeostasis were considered. A software tool was developed to calculate the crash reduction potential of the selected ADAS for the scenarios 2025, 2030 and 2040. The results show that the ADAS related to warning/braking have the greatest future reduction potential and could lead to a reduction of up to 8,700 crashes and 70 fatalities in Austria in 2040. In addition, the Intelligent Speed Assistance system would lead to an overall crash reduction of 8% compared to current crash numbers in Austria in 2040. The Turning Assistant for heavy goods vehicles shows the lowest reduction in crashes and casualties, but due to the highest severity per crash (93 fatalities per 1,000 crashes), it nevertheless provides an important contribution to the reduction of fatalities in road traffic. However, to benefit from the ADAS safety potential, it is highly relevant that these systems are used in a correct manner. In the future, it will be necessary to provide users with more information on the correct use, benefits and limitations of the respective ADAS and to integrate the use of these systems into driver education procedures and tests.

Camera-based object detection is integral to advanced driver assistance systems (ADAS) and autonomous vehicle research, and RGB cameras remain indispensable for their spatial resolution and color information. This study investigates exposure time optimization for such cameras, considering image quality in dynamic ADAS scenarios. Exposure time, the period during which the camera sensor is exposed to light, directly influences the amount of information captured. In dynamic scenarios, such as those encountered in typical driving scenarios, optimizing exposure time becomes challenging due to the inherent trade-off between Signal-to-Noise Ratio (SNR) and motion blur, i.e., extending exposure time to maximize information capture increases SNR, but also increases the risk of motion blur and overexposure, particularly in low-light conditions where objects may not be fully illuminated. The study introduces a comprehensive methodology for exposure time optimization under various lighting conditions, examining its impact on image quality and computer vision performance. Traditional image quality metrics show a poor correlation with computer vision performance, highlighting the need for newer metrics that demonstrate improved correlation. The research presented in this paper offers guidance into the enhancement of single-exposure camera-based systems for automotive applications. By addressing the balance between exposure time, image quality, and computer vision performance, the findings provide a road map for optimizing camera settings for ADAS and autonomous driving technologies, contributing to safety and performance advancements in the automotive landscape. [ABSTRACT FROM AUTHOR]

Ensuring a safe nighttime environmental perception system relies on the early detection of vulnerable road users with minimal delay and high precision. This paper presents a sensor-fused nighttime environmental perception system by integrating data from thermal and RGB cameras. A new alignment algorithm is proposed to fuse the data from the two camera sensors. The proposed alignment procedure is crucial for effective sensor fusion. To develop a robust Deep Neural Network (DNN) system, nighttime thermal and RGB images were collected under various scenarios, creating a labeled dataset of 32,000 image pairs. Three fusion techniques were explored using transfer learning, alongside two single-sensor models using only RGB or thermal data. Five DNN models were developed and evaluated, with experimental results showing superior performance of fused models over non-fusion counterparts. The late-fusion system was selected for its optimal balance of accuracy and response time. For real-time inferencing, the best model was further optimized, achieving 33 fps on the embedded edge computing device, an 83.33% improvement in inference speed over the system without optimization. These findings are valuable for advancing Advanced Driver Assistance Systems (ADASs) and autonomous vehicle technologies, enhancing pedestrian detection during nighttime to improve road safety and reduce accidents. [ABSTRACT FROM AUTHOR]

With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion. [ABSTRACT FROM AUTHOR]

The article examines a 'trinity' of interconnected components by Mobileye, a company moving into autonomous driving. However, Mobileye is neither an automotive manufacturer, nor a nominal 'big tech' company, but an established maker of 'bolt-on' advanced driver assist systems (ADAS), able to draw on over 65 million vehicles. Through these devices, Mobileye is undertaking a 'platformisation', transforming from an automotive supplier into a provider of autonomous vehicle (AV) platforms. We characterise this as a 'car-agnostic' approach to autonomous driving. Mobileye represents the advancement, and modulation, of a platform logic into a different type of hardware: the car. To understand the implications of this, we argue that Mobileye acts parasitically in three ways: by inserting itself between driver and vehicle, vehicle manufacturer and vehicle data, and specific vehicles and the emerging AV industry. [ABSTRACT FROM AUTHOR]

Research on autonomous vehicles has been at a peak recently. One of the most researched aspects is the performance degradation of sensors in harsh weather conditions such as rain, snow, fog, and hail. This work addresses this performance degradation by fusing multiple sensor modalities inside the neural network used for detection. The proposed fusion method removes the pre-process fusion stage. It directly produces detection boxes from numerous images. It reduces the computation cost by providing detection and fusion simultaneously. By separating the network during the initial layers, the network can easily be modified for new sensors. Intra-network fusion improves robustness to missing inputs and applies to all compatible types of inputs while reducing the peak computing cost by using a valley-fill algorithm. Our experiments demonstrate that adopting a parallel multimodal network to fuse thermal images in the network improves object detection during difficult weather conditions such as harsh winters by up to 5% mAP while reducing dataset bias during complicated weather conditions. It also happens with around 50% fewer parameters than late-fusion approaches, which duplicate the whole network instead of the first section of the feature extractor. [ABSTRACT FROM AUTHOR]

• Examined perceptions towards traditional and technology-based speeding interventions. • Low-level speeding was a common behaviour. • Police enforcement perceived as most effective for complying with speed limits. • ADAS perceived as effective for complying with speed limits in 100 km/hr urban area. • Recommendation to educate drivers about how ADAS can help with speed compliance. Introduction: Speeding behaviour contributes significantly to road crashes and subsequent injuries and fatalities. The purpose of this study was to examine which traditional countermeasures (i.e., police enforcement and on-road signs) and technology-based countermeasures (i.e., advanced driver assistance systems [ADAS] and in-vehicle speed audio alerts) drivers perceived as effective in assisting them to comply with posted speed limits. Methods: Participants (N = 680; M age = 49.34 years) who held a current driver's licence completed a 20-minute online survey. Participants in the experimental condition were randomly assigned to read one of four scenarios which differed based on location (urban or regional) and posted speed limit (60 km/hr or 100 km/hr) or to the control condition (no scenario), before answering questions about the perceived effectiveness of the traditional and technology-based interventions. For the experimental conditions, participants were instructed to respond to these questions based on how they would drive in the situation outlined in the scenario. Results: Low-level speeding behaviour was common, with 40.7 % reporting regularly driving 5 km over the posted speed limit in a 60 km/hr speed zone and 50.4 % reporting regularly driving 5 km over the posted speed limit in a 100 km/hr speed zone. A mixed ANOVA revealed that participants perceived police enforcement activities to be the most effective at assisting them to comply with posted speed limits when compared to other traditional and technology-based approaches. Further, ADAS was rated by participants as significantly more effective at assisting them to comply with the posted speed limit in the 100 km/hr urban condition compared to the 60 km/hr urban condition. Conclusions: Low-level speeding behaviour remains common practice and there is a need for continued roadside police presence to discourage this behaviour. Drivers with ADAS-equipped vehicles could also be encouraged to use systems, such as adaptative cruise control, to assist with speed management on high-speed roads. [ABSTRACT FROM AUTHOR]

In recent years, significant advances have been made in the development of Advanced Driver Assistance Systems (ADAS) and other technology for autonomous vehicles. Automated object detection is a crucial component of autonomous driving; however, there are still known issues that affect its performance. For automotive applications, object detection algorithms are required to perform at a high standard in all lighting conditions; however, a major problem for object detection is poor performance in low-light conditions due to objects being less visible. This study considers the impact of training data composition on object detection performance in low-light conditions. In particular, this study evaluates the effect of different combinations of images of outdoor scenes, from different times of day, on the performance of deep neural networks, and considers the different challenges encountered during the training of a neural network. Through experiments with a widely used public database, as well as a number of commonly used object detection architectures, we show that more robust performance can be obtained with an appropriate balance of classes and illumination levels in the training data. The results also highlight the potential of adding images obtained in dusk and dawn conditions for improving object detection performance in day and night. [ABSTRACT FROM AUTHOR]

An advanced driving assistant system (ADAS) is critical for improving traffic efficiency and ensuring driving safety. By anticipating the driver's steering intentions in advance, the system can alert the driver in time to avoid a vehicle collision. This paper proposes a novel end-to-end dual-branch network (EDNet) that utilizes both in-cabin and out-of-cabin data. In this study, we designed an in-cabin driver intent feature extractor based on 3D residual networks and atrous convolution, which is applicable to video data and is capable of capturing a larger range of driver behavior. In order to capture the long-term dependency of temporal data, we designed the depthwise-separable max-pooling (DSMax) module and combined it with a convolutional LSTM to obtain the road environment feature extractor outside the cabin. In addition, to effectively fuse different features inside and outside the cockpit, we designed and propose the dynamic combined-feature attention fusion (D-CAF) module. EDNet employs a freeze-training method, which enables the creation of a lightweight model while simultaneously enhancing the final classification accuracy. Extensive experiments on the Brain4Cars dataset and the Zenodo dataset show that the proposed EDNet was able to recognize the driver's steering intention up to 3 s in advance. It outperformed the existing state of the art in most driving scenarios. [ABSTRACT FROM AUTHOR]

In the present study road safety impact analysis for certain advanced driver assistance systems (ADAS) was conducted. Based on a literature review, expert interviews and current adaptations in legislation, the most promising nine ADAS were selected. The impact was analysed based on statistical crash data from Austria. Factors such as infrastructure and weather conditions, market penetration, expected functionality of sensors, user acceptance and risk homeostasis were considered. A software tool was developed to calculate the crash reduction potential of the selected ADAS for the scenarios 2025, 2030 and 2040. The results show that the ADAS related to warning/braking have the greatest future reduction potential and could lead to a reduction of up to 8,700 crashes and 70 fatalities in Austria in 2040. In addition, the Intelligent Speed Assistance system would lead to an overall crash reduction of 8% compared to current crash numbers in Austria in 2040. The Turning Assistant for heavy goods vehicles shows the lowest reduction in crashes and casualties, but due to the highest severity per crash (93 fatalities per 1,000 crashes), it nevertheless provides an important contribution to the reduction of fatalities in road traffic. However, to benefit from the ADAS safety potential, it is highly relevant that these systems are used in a correct manner. In the future, it will be necessary to provide users with more information on the correct use, benefits and limitations of the respective ADAS and to integrate the use of these systems into driver education procedures and tests. [ABSTRACT FROM AUTHOR]

This short review highlights the main sensor structures that are used in the ADAS (Advanced Driver Assisted System) field, in order to outline the progress at this moment (2023). Starting from this achieved level, we have proposed another approach to the ADAS problematics, regarding the prospects for development after the current moment, a different perspective than the one that has AV (Autonomus Vehicle) as its endpoint. Computer Assisted Driving represents a necessary and sufficient solution for increasing traffic safety in the near future. The integrated driver concept in the assisted environment implies a symbiotic human-machine collaboration. This collaboration requires a psychological acceptance of the ADAS system on behalf of humans, as an essential part of the current and future automobile. The progress highlighted by the documentation undertaken allows us to imagine a concept of a symbiotic human-machine system, based on the communication between ADAS and an external computing resource that assists the driver, based on the long analysis of recorded car events. Future automobiles must include an ADAS system that provides a subtle human-machine synapse, based on a series of computerized warnings to the driver generated in a timely manner, resulting from the high predictive capacity of AI-DLM algorithms. The computing speed of the local ADAS algorithms will be supplemented by the external server processing resource, accessed at the right time, to resolve an unexpected deadlock in the car roll. [ABSTRACT FROM AUTHOR]

This paper presents Multiple Traffic Light Advisor (MTLA), a novel Green Light Optimal Speed Advisory (GLOSA) system that leverages 5G communication technology. GLOSA systems are emerging as a key component in intelligent transportation systems, thanks to the development of effective communication technologies. At its core, MTLA serves as a guidance system for drivers, providing real-time instructions to adjust vehicle speed to optimize the utilization of current and future states of traffic lights along their route.The work addresses several limitations in the current state-of-the-art approaches, including the use of an overly simplified velocity profile, the omission of potential grip and jerk in problem formulation, and the absence of a detailed description of the algorithm’s implementation aspects. Initially, we comprehensively present an optimization-free implementation of the overall control architecture based on an unconventional speed profile. Subsequently, MTLA is improved within a non-linear Model Predictive Control (MPC) framework which uses the latter nonoptimal solution as an initial guess and considers potential grip and jerk in the problem formulation. The developed systems are numerically tested and compared within a high-fidelity simulation environment using the IPG CarMaker simulator. The results demonstrate promising performance in terms of energy savings, with a significant reduction of 37% in energy usage, as well as improved overall comfort with respect to the case where no guidance is given to the driver. These findings suggest a high potential for future developments in this domain. [ABSTRACT FROM AUTHOR]

Lane detection is a fundamental component of advanced driver assistance systems, facilitating critical functionalities like Lane Keep/Change Assistance, Lane Departure Warning, Adaptive Cruise Control, and Vehicle Localization. Despite significant advancements in camera-based lane detection, it continues to confront challenges that can be effectively addressed with LiDAR technology. This research contributes to the domain of LiDAR-based lane detection across three pivotal areas. Firstly, we introduce the BoostedDim Attention method, enhancing traditional Multi-Head Self-Attention (MHA) calculations within the shallow Vision Transformers-based K-Lane baseline model. This method excels, particularly in demanding scenarios, including unknown and nighttime conditions at short ranges (0–30 m) and daytime scenarios for long ranges (30–50 m). Secondly, we devise distance-based True Positive Rate (TPR) and Lateral Error evaluation metrics, providing a more precise and tailored approach to evaluating model performance compared to conventional metrics. These metrics consider sensor-specific and task-specific factors, offering a comprehensive assessment of LiDAR-based lane detection capabilities. Lastly, our investigation sheds light on the significance of calibrated reflectivity and intensity data, revealing their impact on lane detection under various lighting conditions. Notably, we highlight the positive influence of intensity data in low-light conditions for short ranges and its adverse effect during daytime for long ranges. These findings have significant implications for enhancing autonomous driving applications and other computer vision tasks.

Snow accumulation on surfaces exposed to adverse weather conditions has been studied over the years due to a variety of problems observed in different industry sectors, such as aeronautics and wind and civil engineering. With the growing interest in autonomous vehicles (AVs), this concern extends to advanced driver-assistance systems (ADAS). Weather stressors, such as snow and icing, negatively influence the sensor functionality of AVs, and their autonomy is not guaranteed by manufacturers during episodes of intense weather precipitation. As a basis for mitigating the negative effects caused by heavy snowfall, models need to be developed to predict snow accumulation over critical surfaces of AVs. The present work proposes a framework for the study of snow accumulation on road vehicles. Existing icing and snow accretion models are reviewed, and adaptations for automotive applications are discussed. Based on the new capabilities developed by the Weather on Wheels (WoW) program at Ontario Tech University, a model architecture is proposed in order to progress toward adequate snow accretion predictions for autonomous vehicle operating conditions, and preliminary results are presented. [ABSTRACT FROM AUTHOR]

Road-object detection, recognition, and tracking are vital tasks that must be performed reliably and accurately by self-driving car systems in order to achieve the automation/autonomy goal. Other vehicles are one of the main objects that the egocar must accurately detect and track on the road. However, deep-learning approaches proved their effectiveness at the expense of very demanding computational power and low throughput. They must be deployed on expensive CPUs and GPUs. Thus, in this work, a lightweight vehicle detection and tracking technique (LWVDT) is suggested to fit low-cost CPUs without sacrificing robustness, speed, or comprehension. The LWVDT is suitable for deployment in both advanced driving assistance systems (ADAS) functions and autonomous-car subsystems. The implementation is a sequence of computer-vision techniques fused together and merged with machine-learning procedures to strengthen each other and streamline execution. The algorithm details and their execution are revealed in detail. The LWVDT processes raw RGB camera pictures to generate vehicle boundary boxes and tracks them from frame to frame. The performance of the proposed pipeline is assessed using real road camera images and video recordings under different circumstances and lighting/shading conditions. Moreover, it is also tested against the well-known KITTI database, achieving an average accuracy of 87%. [ABSTRACT FROM AUTHOR]

As the aging population grows and technology persists, autonomous vehicles (AVs) present a significant opportunity to enhance safety and mobility for the elderly. This qualitative literature review identifies the specific challenges faced by elderly drivers in safety and technical concerns. The study identifies four emerging themes: financial barriers to AV market access, resistance to autonomous vehicle trust, quality of life, improvements, and cognitive decline in driving abilities. The results reveal the benefits AVs can offer, however high cost and trust issues among the elderly prevent their broad acceptance and usage. The study highlights the requirement of developing user interfaces that are more accessible for the elderly, implementing pricing strategies that consider their financial constraints, and establishing AV educational programs. These strategies are essential for enabling elderly drivers to AV technology. This allows the elderly to leverage AV innovations to maintain their independence and improve their overall quality of life. [ABSTRACT FROM AUTHOR]

In an effort to improve automated driving and advanced driver assistance systems (AD/ADAS) performance, the industry is gradually moving toward greater vehicle autonomy. In this context, particular emphasis is being placed on the use of in-vehicle sensors and cameras. While these systems are primarily intended to replace or support the driver with invehicle devices, the ability to actively perceive non-visible areas can significantly improve vehicle safety and optimize route decisions. Traditional navigation maps (SD-MAP) have served their purpose, but often lack the necessary dynamism and accuracy; Vehicle-to-Everything (V2X) technology, particularly in the area of Vehicle-to-Network (V2N), is a growing area of interest due to the proliferation of smart phones and offer promising alternatives that leverage the strength of cellular networks. This study will evaluate the utility of smartphones as a central element of vehicle sensing and investigate their potential to provide critical lane-level recognition data for next-generation AD/ADAS applications. Through a comprehensive system architecture, smartphone GNSS data is processed to estimate lane-specific traffic conditions. In addition, current limitations and potential enhancements with respect to the use of OpenStreetMap (OSM) data will be detailed. While the accuracy of lane identification based solely on data from smartphones remains a challenge, this paper explores possible countermeasures, highlighting the integration of various data sources to achieve a holistic vehicle control system. [ABSTRACT FROM AUTHOR]

Eighty percent of traffic accidents are caused by human error, called hypo vigilance, stemming from drowsiness, stress, or distraction while driving. This poses a significant threat to road safety. An electrocardiogram (ECG) is often used to monitor drivers' health. Thus, enhancing vehicles with Internet of Things (IoT) sensors and local analytical databases becomes crucial for real-time detection and transmission of relevant health data to avoid things that compromise road traffic safety. This study introduces a cost-effective in-vehicle ECG sensing prototype using an AD8232 sensor integrated with an Arduino Uno and an AD8232 Wi-Fi module placed on the steering wheel to monitor the driver's heart signal while driving. Short-term heart rate variability (HRV) features were computed through Python from the acquired ECG data, and supervised machine learning techniques such as AdaBoost, Random Forest, Naïve Bayes, and Support Vector Machine (SVM) classified the features into normal and abnormal classes. Naive Bayes exhibited the highest accuracy (90.91%) and F1 score (85.71%), surpassing Random Forest's lower accuracy (63.64%) and F1 score (50.00%). These findings indicate the prototype's potential as a valuable tool for ensuring safe and efficient driving, proposing integration into standard vehicle safety systems for enhanced road traffic safety. [ABSTRACT FROM AUTHOR]

The continuous development and importance of the Field of road transport these days make it necessary to design, develop and implement technological solutions that reduce (eliminate as much as possible) the risk of road accidents. Such a technological solution is also represented by advanced driver assistance systems (ADAS), systems that assist drivers in various ways, such as collision avoidance, automatic parking, adaptive cruise control, attention and lane departure warnings. Over the next ten years, there will likely be a rise in the need for ADAS system deployment in automobile construction, driven by consumer and regulatory interest in safety applications that protect drivers and lower accident rates. At the moment, autonomous emergency braking and forward collision warning systems are mandated for all cars in the US and the EU. Additionally, advanced driver assistance systems (ADAS) may soon distinguish automobile brands and have a significant impact on consumer preference. The present work aims to provide a general picture related to the current research and development of ADAS systems that refer to the detection of the traffic lane and lane markings. The approaches are presented regarding: the current development directions of ADAS systems, current traffic lane detection techniques, traffic lane detection methods and the use of artificial intelligence techniques in this field. The general conclusion is that further research is needed in the field, to increase the performance of traffic lane detective systems by using advanced algorithms and easy-to-implement methods that do not require large hardware resources. [ABSTRACT FROM AUTHOR]

This paper describes the development of an Advanced Driver Assistance System (ADAS) which will allow drivers to avoid collisions with an oncoming vehicle from an occluded area when turning right at intersections in left-hand traffic. Connected vehicles, in coordination with infrastructure, represent one of the commercialized ADAS technologies for collision avoidance. However, the coverage of the ADAS will be limited to designated intersections only, as communication equipment needs to be installed in both the vehicle and infrastructure to enable the assistance. This paper proposes an ADAS using on-board sensors, independent of infrastructure facilities, to control the vehicle velocity to avoid collisions. Most current intersection assistance, by using an Autonomous Emergency Braking System (AEBS), allows the driver to avoid a collision with oncoming vehicles when there is clear vision without occlusion. However, many accidents occur when the vehicle detects the oncoming vehicle too late because of occlusion in the intersection, such as a vehicle in the opposite lane. This system calculates the hazardous speed criteria of the ego vehicle, which might result in a high risk of collision when darting out occurs, and provides speed control assistance to allow the driver to escape from the hazardous speed region. The simulation results reveal that the proposed system effectively reduces the possibility of collisions compared to conventional AEBS. [ABSTRACT FROM AUTHOR]

Understanding the complexities of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technologies is critical for road safety, especially concerning their adoption by drivers. Effective training is a crucial element in ensuring the safe and competent operation of these technologies. This study emphasises the critical role of training methodologies in shaping drivers' mental models, defined as an individual's cognitive frameworks for understanding and interacting with ADAS and AV systems. Their mental models substantially influence their interactions with those technologies. A comparative analysis of text-based and video-based training methods has been conducted to assess their influence on participants' performance and the development of their mental models of ADAS and AV functionalities. Performance is evaluated in terms of the accuracy and reaction time of the participants as they interacted with ADAS and AV functions in a driving simulation. The findings reveal that video-based training yielded better performance outcomes, more accurate mental models, and a deeper understanding of ADAS functionalities among participants. These findings are crucial for policy makers, automotive manufacturers, and educational institutions involved in driver training. They underscore the necessity of developing tailored training programs to facilitate the proficient and safe operation of increasingly complex automotive technologies. [ABSTRACT FROM AUTHOR]

The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver's alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle's commands. [ABSTRACT FROM AUTHOR]

Object detection has played a crucial role in Advanced Driver Assistance Systems (ADAS) applications, particularly with integrating deep learning techniques. These advancements have improved ADAS applications by enabling more precise object identification, thereby enhancing real-time decision-making. Object detection models can be categorized into two main groups: two-stage and one-stage models. While prior studies reveal that one-stage detectors generally achieve higher frames per second (FPS) at the expense of some accuracy, they remain better suited for real-time ADAS applications. Our study aims to analyze the performance of an object detection model created using SSD-MobileNet, a one-stage detector approach. We focused on identifying road-related objects such as vehicles, and traffic signs. The contribution of our work lies in developing an object detection model using a pre-trained SSD-MobileNet and employing transfer learning. This process involves introducing a new fully connected layer tailored for the specific identification of objects in road scenes. The retraining of the SSD-MobileNet model is executed through GPUaccelerated transfer learning on the MS COCO dataset, incorporating appropriate pre-processing to exclusively include road-related objects. Our findings indicate promising results for the retrained SSD-MobileNet model, achieving an F1 score of 0.801, and a Mean Average Precision (mAP) of 65.41 at 71 FPS. A comparative analysis with other one-stage and two-stage detectors demonstrates the model's performance, surpassing some existing works in the literature related to road object detection. Notably, our model exhibits improved mAP while maintaining a higher FPS, rendering it more apt for ADAS applications. [ABSTRACT FROM AUTHOR]