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Due to increasing mobility and energy conservation needs, improving bus and coach safety without adding weight is essential. Many crashes with fatal outcomes for vehicle occupants are associated with the rollover of the vehicle, revealing the structural weakness of the steel pillars between windows, which must resist high levels of bending during rollovers. This study aims to reinforce these pillars with expired carbon fiber prepreg from the aircraft industry, improving safety and reducing environmental waste. To manufacture the pillars, shot-blasted hollow S275 steel tubes with a side length of 25 mm and a thickness of 1.5 mm were used. Bidirectional GG600T woven carbon fiber, CF, and aircraft-grade recycled carbon fiber-reinforced plastic, rCFRP, prepreg M21EV/IMA/3 were used as composite reinforcements. The first composite was made from a CF weave using the rigid epoxy resin Sicomin® 8500/Sicomin® SD8601. The rCFRP composite was frayed, and a new composite was made with the same rigid epoxy resin. Both composites were joined to the steel tube using a tough structural adhesive (SikaPower® 1277). A third composite was obtained using the frayed rCFRP and the structural adhesive as a polymer matrix. All composites were treated with an APPT (atmospheric-pressure plasma torch) before being joined to the steel pillar with the structural adhesive. The comparison of the three reinforcements showed that the steel reinforced with the recycled prepreg composite manufactured with the rigid adhesive performed best, with a 50% increase in specific bending strength and only a 32% increase in weight. It also absorbed 71% more energy, which shows that this novel option for upcycling can noticeably increase the crashworthiness of structures. [ABSTRACT FROM AUTHOR]

With over 1.6 million traffic deaths in 2016, automated vehicles equipped with automated driving systems (ADSs) have the potential to increase traffic safety by assuming human driving tasks within the operational design domain (ODD). However, safety validation is challenging due to the open-context problem. Current strategies, such as pure driving and requirement-based testing, are insufficient. Scenario-based testing offers a solution but necessitates appropriate scenario selection, testing methods, and evaluation criteria. This paper builds upon a method to calculate the covered ODD using tested scenarios generated from logical scenarios, considering parameter discretisation uncertainty. Acceptance criteria for the safety argumentation are proposed based on parameter space coverage and variance introduced via discretisation, thus contributing to quantifying the residual risks of safety validation. The approach is demonstrated through two logical scenarios with probability density functions of the parameters generated using a trajectory dataset. These criteria can serve as risk acceptance criteria, providing comparability and explainable results. By developing a robust scenario-based testing approach, ADS safety can be validated, leading to increased traffic safety and reduced fatalities. Since ADSs incorporate AI models, this proposed validation strategy can be extended to AI systems across multiple domains for the respective assurance argument required for deployment. [ABSTRACT FROM AUTHOR]

The safety inspection system for the underside of vehicles demands both high detection speed and accuracy, necessitating a model with small parameters and high network efficiency. To address these issues and improve the real-time performance of under-vehicle safety inspection, the YOLOv8n object-detection algorithm was enhanced, resulting in the PSP-YOLO algorithm. Firstly, the improved PSP-YOLO network introduces a P2 small-object detection head, which is particularly effective in detecting tiny objects, such as small dangerous objects under vehicles, thereby significantly enhancing the ability to detect such small targets. Secondly, the Space-to-Depth Conv (SPD-Conv) module was introduced into the backbone network, which improves the detection performance of low-quality samples and small objects while ensuring high precision and accurate localization of small targets in images. Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model's parameters and computational load, conserving computing resources, and improving detection speed and accuracy. Experimental results demonstrate that the modified model increased mAP@0.5 by 3.9% and mAP@0.5-0.95 by 3.6% on the dataset used, reduced the number of parameters to 2.53 M, and achieved a detection speed of 104.6 f/s. Therefore, the improved YOLOv8n algorithm significantly enhances the detection performance of dangerous objects, meeting the high-precision and real-time requirements for vehicle underside safety inspections. [ABSTRACT FROM AUTHOR]

Safety is one of the essential and vital factors in the case of the automobile industry. The vehicle has several safety features, like airbags, seat belts, antilock brakes, etc. The car's crash box is one of the crucial safety features that absorb the initial impact. The radiator, intercooler, fender, and hood are expensive parts of a car. A crash box can shield these components. A crash box can reduce the likelihood of the occupants dying or suffering significant injuries. The crash box absorbs kinetic energy from the moving car. This absorption lowers the possibility of occupant injuries and fatalities. Consequently, crash boxes are a crucial life-saving device used in cars. The crash box can be made of different (cross-section) shapes. The different shapes and materials of a crash box are investigated in this study. The study takes into account the square, rectangular, hexagonal, and pentagonal shapes. The configurations are simulated for the energy absorption study under impact load. Crash box analysis focuses on selecting the best material for the crash box and investigating its structural designs. The best material is chosen by applying Analytical Hierarchical Process (AHP). [ABSTRACT FROM AUTHOR]

This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle states, an MR damper current controller that only considers the suspension deflection and deflection rate is proposed. A linear matrix inequality problem is formulated to design the current controller, with the objective of enhancing ride safety and comfort while guaranteeing vehicle stability and robustness against any road disturbance. A series of experiments demonstrates the enhanced performance of the proposed MR damper model, which exhibits greater accuracy than other state-of-the-art damper models, such as Bingham or bi-viscous. An evaluation of the vehicle behavior under two simulated road scenarios has been conducted to demonstrate the performance of the proposed output feedback MR damper-based semi-active suspension system. [ABSTRACT FROM AUTHOR]

The primary goal of autonomous vehicles is vehicle safety, achieved through vehicle planning and control based on the understanding of the driving environment. To understand the surrounding environment, most autonomous driving systems utilize lidar and cameras for tracking nearby objects. However, both sensors are effective only within a short range of 50 m and lack robustness in adverse conditions, thereby failing to ensure the stability of object tracking and state estimation. Therefore, to enable continuous tracking over long distances, this paper proposes a robust tracking and detection system based on radar-camera fusion in urban autonomous driving. In this system, radar is used as the primary sensor to achieve uninterrupted tracking over longer distances, and cameras are employed to compensate for the relatively inaccurate measurements of the radar, thereby enhancing tracking accuracy. To achieve robust tracking results in urban environments and from surrounding objects, the track management process utilizes velocity values, unique to radar compared to other sensors, to establish associations between tracklets and measurements. Subsequently, the process selects valid tracklets among those unmatched by considering the relationship between the time of initialization for each tracklet and the current time. To enhance the accuracy of tracking results, the state update process integrates radar and camera information using a decentralized Kalman filter. This filter structure not only improves accuracy but also ensures robustness against sensor failure and real-time performance in the fusion process. The algorithm is implemented in autonomous vehicles equipped with radar and low-cost cameras and is validated on test tracks, as well as in urban and highway environments. The experimental results confirm that the algorithm continuously tracks targets over long distances and demonstrates enhanced tracking results through fusion with the camera, conducting the process efficiently and robustly. [ABSTRACT FROM AUTHOR]

This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle's maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle's inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle's load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections. [ABSTRACT FROM AUTHOR]

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This “out-of-the-loop” problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilitate driver–vehicle cooperation. However, the effectiveness of auditory, visual, or combined signals in improving situational awareness and reaction time for safe maneuvering remains unclear. This study investigates how signal modalities affect drivers’ behavior using virtual reality (VR). We measured drivers’ reaction times from signal onset to take-over response and gaze dwell time for situational awareness across twelve critical events. Furthermore, we assessed self-reported anxiety and trust levels using the Autonomous Vehicle Acceptance Model questionnaire. The results showed that visual signals significantly reduced reaction times, whereas auditory signals did not. Additionally, any warning signal, together with seeing driving hazards, increased successful maneuvering. The analysis of gaze dwell time on driving hazards revealed that audio and visual signals improved situational awareness. Lastly, warning signals reduced anxiety and increased trust. These results highlight the distinct effectiveness of signal modalities in improving driver reaction times, situational awareness, and perceived safety, mitigating the “out-of-the-loop” problem and fostering human–vehicle cooperation.

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.

One of the main causes of death worldwide among young people are car crashes, and most of these fatalities occur to children who are seated in the front passenger seat and who, at the time of an accident, receive a direct impact from the airbags, which is lethal for children under 13 years of age. The present study seeks to raise awareness of this risk by interior monitoring with a child face detection system that serves to alert the driver that the child should not be sitting in the front passenger seat. The system incorporates processing of data collected, elements of deep learning such as transfer learning, fine-tunning and facial detection to identify the presence of children in a robust way, which was achieved by training with a dataset generated from scratch for this specific purpose. The MobileNetV2 architecture was used based on the good performance shown when compared with the Inception architecture for this task; and its low computational cost, which facilitates implementing the final model on a Raspberry Pi 4B. The resulting image dataset consisted of 102 empty seats, 71 children (0-13 years), and 96 adults (14-75 years). From the data augmentation, there were 2,496 images for adults and 2,310 for children. The classification of faces without sliding window gave a result of 98% accuracy and 100% precision. Finally, using the proposed methodology, it was possible to detect children in the front passenger seat in real time, with a delay of 1 s per decision and sliding window criterion, reaching an accuracy of 100%. Although our 100% accuracy in an experimental environment is somewhat idealized in that the sensor was not blocked by direct sunlight, nor was it partially or completely covered by dirt or other debris common in vehicles transporting children. The present study showed that is possible the implementation of a robust noninvasive classification system made on Raspberry Pi 4 Model B in any automobile for the detection of a child in the front seat through deep learning methods such as Deep CNN. [ABSTRACT FROM AUTHOR]

Publicizing safety ratings of vehicles can motivate manufacturers to prioritize safety and help consumers choose safer vehicles, leading to safer fleets. The benefits of primary safety technologies that prevent crash occurrence are not currently incorporated in current ratings in a way that values their safety benefits consistently. We aimed to propose a method for assigning weights for each safety technology to account for established safety benefits using published effectiveness and prevalence from real-life data. To illustrate this method, we present a worked example calculated using crash and injury data from Australia and New Zealand. The method proposed attenuates the weights for given safety technologies where two or more safety technologies fitted to the same vehicle are effective for the same types of crashes. In the worked example using Australasian data, large SUVs were estimated to have the largest safety increment from the fitment of all the technologies considered compared to vehicles without these primary safety technologies, with an almost 17% reduction in crash occurrence. Cars with all the technologies fitted had estimated average crash reduction of between 11% and 12%. Different market groups have different crash patterns, so the safety attributable to safety technology fitment differs at the market group level. This study presents an approach for providing a summary measure of crash avoidance according to the fitment of safety technologies. If this measure is combined with an estimate of secondary safety (whether derived from existing crash and injury data or from new car crash assessment programs), the combined estimate then represents the important elements of safety provided by the vehicle. The methods presented here form a rational basis for assigning safety ratings to represent the benefits of swiftly developing safety technologies. [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]

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This "out-of-the-loop" problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilitate driver–vehicle cooperation. However, the effectiveness of auditory, visual, or combined signals in improving situational awareness and reaction time for safe maneuvering remains unclear. This study investigates how signal modalities affect drivers' behavior using virtual reality (VR). We measured drivers' reaction times from signal onset to take-over response and gaze dwell time for situational awareness across twelve critical events. Furthermore, we assessed self-reported anxiety and trust levels using the Autonomous Vehicle Acceptance Model questionnaire. The results showed that visual signals significantly reduced reaction times, whereas auditory signals did not. Additionally, any warning signal, together with seeing driving hazards, increased successful maneuvering. The analysis of gaze dwell time on driving hazards revealed that audio and visual signals improved situational awareness. Lastly, warning signals reduced anxiety and increased trust. These results highlight the distinct effectiveness of signal modalities in improving driver reaction times, situational awareness, and perceived safety, mitigating the "out-of-the-loop" problem and fostering human–vehicle cooperation. [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]

The fusion of human-centric design and artificial intelligence capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond traditional transportation. These vehicles can dynamically interact with passengers and adapt to their preferences. This article proposes a novel framework that leverages large language models (LLMs) to enhance the decision-making process in autonomous vehicles. By utilizing LLMs’ contextual understanding abilities with specialized tools, we aim to integrate the language and reasoning capabilities of LLMs into autonomous vehicles. Our research includes experiments in HighwayEnv, a collection of environments for autonomous driving and tactical decision-making tasks, to explore LLMs’ interpretation, interaction, and reasoning in various scenarios. We also examine some well-defined real-time personalized driving tasks, demonstrating how LLMs can influence driving behaviors based on drivers’ verbal commands. Our empirical results highlight the substantial advantages of utilizing chain-of-thought prompting, leading to improved driving decisions and showing the potential for LLMs to enhance personalized driving experiences through ongoing verbal feedback. The proposed framework aims to transform autonomous vehicle operations, offering personalized support, transparent decision making, and continuous learning to enhance safety and effectiveness. We achieve user-centric, transparent, and adaptive autonomous driving ecosystems supported by the integration of LLMs into autonomous vehicles. Experiment videos are available at https://youtube.com/playlist?list=PLgcRcf9w8BmLJi_fqTGq-7KCZsbpEIE4a&si=dhH9lgaeSmB5K94t. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) functionalities are increasingly being used in vehicle applications. While current product development models take the increasing proportion of software into account, the special requirements of artificial intelligence developments are hardly ever explicitly considered. The new requirements result both from increasing standardisation and regulation and from the iterative and explorative approach inherent in AI model development. This paper identifies the key adaptations to the standard automotive product development process that are required to cover the requirements of AI development. The adapted development model was trialled in two vehicle developments, the most important lessons learnt of which are summarised in this paper.

The Enhanced Car Safety System is a novel approach to improving road safety by integrating real-time notifications to emergency services, including hospitals and police departments, in the event of a vehicular accident. This system employs advanced sensors and telematics to detect collisions and other critical incidents automatically. Upon detecting an accident, the system promptly gathers vital data such as the vehicle's location, the severity of the impact, and the number of passengers. This information is then transmitted to nearby emergency services through a secure communication network. The primary goal of this project is to reduce response times of emergency personnel, thereby increasing the chances of survival and reducing the severity of injuries for accident victims. The system leverages GPS, accelerometers, and IoT (Internet of Things) technology to ensure accurate and swift notification. Additionally, it includes features to avoid false alarms, such as verifying collision data against predefined criteria before sending alerts. The Enhanced Car Safety System aims to bridge the gap between accident occurrence and emergency response, providing a critical time advantage that can save lives and enhance overall road safety. By integrating this system into modern vehicles, we anticipate a significant reduction in fatalities and serious injuries on the road, making driving a safer experience for all. [ABSTRACT FROM AUTHOR]

A combined steering and braking collision avoidance control method based on model predictive control is proposed in the present paper. This method can overcome the limitations of single steering or braking collision avoidance and improve vehicle safety and tracking performance in extremely complex conditions. The utilization rate of tire force, path tracking performance, and driving stability are considered in the design of the model predictive control (MPC) optimization objective. In addition, the front wheel angle and four-wheel braking torque are taken as the optimization variables. Simulation results showed that the proposed method can greatly improve the path tracking performance and driving stability of vehicles. [ABSTRACT FROM AUTHOR]

This study presents an investigation of autonomous emergency braking pedestrian (AEB-P) system performance during harsh braking on wet road pavement. The system was designed to consider a pedestrian walking in front of the host vehicle. The performance of the AEBP system would degrade immediately as the pads on the brakes become worn, and the vehicle continues to brake on a wet road surface. The vehicle conditional artificial potential field (VC-APF) is an innovative approach for motion planning in the AEB-P introduced in this work. The simulation was performed to explore the impact of brake pad degradation on VC-APF effectiveness on wet road pavement. The first evaluation involved a test to evaluate the effectiveness of the risk assessment in the AEB-P system when encountering a moving obstacle (pedestrian). The second test evaluated VC-APF performance, for instance, the vehicle's safety distance when the vehicle performed hard braking at 0.4, 0.35, and 0.24 brake pad friction coefficients. The third evaluation focused on the vehicle’s speed behavior during deceleration at various brake pad friction coefficients. The simulation results showed that while braking at 0.4 and 0.35 brake pad friction coefficients, the vehicle maintained a minimum safety distance of 1.5 m and 0.69 m from a pedestrian on wet road pavement, respectively. However, the brake pad friction coefficient of 0.24 failed to prevent the vehicle from crashing. The findings indicate that an exhausted brake pad reduces the vehicle's safety. [ABSTRACT FROM AUTHOR]

This study aimed to investigate and provide insight into the drivers and motivations behind users' intentions for using car dashcams. The study utilized a purposive sampling technique with a sample size of 225 and obtained responses through a structured questionnaire. Partial least squares structural equation modeling (PLS-SEM) was performed to verify the proposed hypotheses. The findings demonstrated that the perceived quality of the system, the quality of the information, and the quality of the interaction positively influenced the perceived ease of use and usefulness of using car dashcams. Attitude and behavioral intention were affected by perceived ease of use and usefulness, while perceived usefulness had no influence on intention to use car dashcams. While hedonic motivation and price value impacted behavioral intention, social intention did not affect it. Attitude was found to fully mediate the path between perceived usefulness and behavioral intention. The outcomes of the study incorporate a considerable amount of knowledge into the adoption behavior of telematics literature by integrating prevalent technology adoption models into a unified model: The information system success model (ISS), technology acceptance model (TAM), and unified theory of acceptance and use of technology 2 (UTAUT2). The findings provide valuable insights for marketers, the dash cam industry, and policymakers to enhance their understanding of the critical factors affecting the adoption of car dashcams, which can assist in customizing their marketing campaigns and policies to encourage the usage of dashcams, leading to improved road safety and safe driving behavior. [ABSTRACT FROM AUTHOR]

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Vehicle safety on roadsides is vital for preventing collisions, controlling failures and accidents, and ensuring driver and passenger wellness. Finite Element Analysis (FEA) in vehicle safety relies on the vehicle's physical attributes for predicting and preventing collisions. This article introduces a Differential FEA (DFEA) model for predicting vehicle collisions regardless of the speed and direction for driver/ passenger safety. The proposed model induces a vehicle's speed, direction, and displacement from two perspectives: self and approaching vehicle. The collision probability with the trailing or persuading vehicle is calculated by distinguishing the forward and rear features. The differential calculus for the point of deviation and distance metrics are significantly estimated for a vehicle's front and rear ends. Such calculus generates a maximum and minimum possibility for self and approaching vehicle contact. This contact is further split based on the collision threshold; the threshold is determined using the safe distance between two vehicles for collision-less driving. The threshold exceeding vehicles are alerted for their change in direction/ speed through contact point (rear/front) differential derivatives. This ensures collision detection under fewer contact errors, leveraging the safety of the duo vehicles. [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]

Accurate distance estimation is crucial for ensuring the safety and efficiency of intelligent and future autonomous mobility. In this paper, we propose a method for real-time estimation of visible lane distance based on image data only from a single camera and an existing road lane marking estimation system. By leveraging camera intrinsic and height above the road, we utilize lane parallelism and geometric properties of frames from a camera to compute the distance to the furthest visible lane point. Intelligent recalculations of horizon level are performed for every frame, accommodating variations encountered during uphill or downhill driving scenarios. Moreover, the proposed solution is designed to be theoretically adaptable to gauge the distance to any detected object on the camera frame, not only road lanes. We evaluate the effectiveness of our method on a dataset created for this purpose, assessing estimation performance through comparison with ground truth data obtained from high-precision GPS measurements. The results indicate comparable estimation performance with other works. Coupled with the capability for real-time implementation in vehicles, we underscore the potential of our approach for advanced driver assistance systems (ADAS) in various aspects, such as safe driving speeds, obstacle avoidance, or collision prevention. This research contributes to the ongoing efforts in developing robust and practical solutions for autonomous mobility applications.

The objective of the paper is to determine the relationship between a vehicle price and its safety ratings in the Czech Republic for vehicles sold and newly registered in the year 2022. The data collection was performed using content analysis. It was found that in 2022, Škoda Auto vehicles accounted for more than 35 % of all newly registered vehicles in the Czech Republic. The evaluation of vehicle safety was performed on the basis of results published on the EuroNCAP server. The findings were used as a basis for performing cluster analysis and correlation analysis of the data obtained. The analysis shows that even though safety plays an important role in the automotive industry, vehicle price is not a decisive factor in achieving higher safety ratings. The findings of this paper have a significant impact on consumers and automotive, showing that the price difference between vehicles does not necessarily correlate with different levels of safety. The limitation of this paper is its focus on the Czech Republic only, which may influence the generalization of the results to other markets.

This survey aims to provide an overview of various methods for generating data-driven test scenarios for assessing automated driving systems (ADSs). The survey updates the overall process of scenario generation and categorizes the current methods using a systematic literature review of 64 studies identified between 2017 and 01/2023. Overall, we demonstrate that the data-driven scenario generation process should be updated by another process step, scenario fusion, leading to seven process steps: 1) scope definition, 2) primary data source selection, 3) primary data collection, 4) scenario identification, 5) scenario fusion, 6) scenario generation, and 7) scenario evaluation. “Scenario fusion” aims to fuse scenarios identified in different data sources for a better coverage of the ADSs’ operational design domains (ODDs) and a more comprehensive scenario description. Moreover, we introduce an improved definition for the representativity of test scenario catalogs, which helps improve the collection of traffic data using sampling plans. Also, we show that real driving and police accident data are the most commonly used data input sources. Besides, we illustrate that the ODD is often not defined. Finally, we discuss that the standardization of test scenario generation is difficult because most methods do not address specific ADSs and test environments, and do not provide standardized interfaces. Overall, we recommend comparing existing approaches using the same input data and researching the mutual supplementation of the existing methods. Finally, pre-defined case studies, further standardized terminology, and standards for test execution and evaluation can help speed up the standardization process.

The safety approval of Highly Automated Vehicles (HAV) still faces the automotive industry with economic and legal challenges. For verification and validation, it is essential to describe the intended behavior of an HAV in the development process in order to prove safety. The demand for this behavior comes from the traffic rules which are instantiated by the present scenery around the vehicle (e.g. traffic signs or road markings). The Operational Design Domain (ODD) specifies the scenery in which an HAV may operate, but current descriptions fail to explicitly represent the associated behavioral demand of the scenery. We propose a new approach for a Behavior-Semantic Scenery Description (BSSD) in order to describe the behavior space of a present scenery. A behavior space represents the delimitation of the legally possible behavior. The BSSD explicitly links the scenery with the behavioral demand for HAV. Based on identified goals and challenges for such an approach, we derive requirements for a generic structure of the description for complete road networks. All required elements to represent the behavior space of the scenery are identified. Within real world examples, we present an instance of the BSSD integrated into the HD-map framework Lanelet2 to prove the applicability of the description. The presented approach supports development, test and operation of HAV by closing the knowledge gap of where a vehicle has to behave in which limits within an ODD.

Automated driving systems should be able to avoid road traffic accidents and drive more safely than human drivers do. Test scenarios derived from real-world data such as police accident data can help assess the safety performance of automated driving systems. In many countries, the police collect data and information about nearly every accident, resulting in a representative sample. However, the collected accident data often do not contain the exact conflicts that cause an accident. Therefore, we estimated the globally known three-digit accident type for German police accident data, describing accident-causing conflicts. The data supplemented by the estimated three-digit accident type can then be used in subsequent test scenario generation. Accordingly, this study presents the first classification model for predicting 30 types of turning accidents. We tested a CatBoost model and a large-language model called BERT, using different feature sets and model designs. Overall, the CatBoost model performed best when using accident descriptions and non-text features such as collision type. Anomaly detection performed before model training revealed additional knowledge-driven miscoding in the police data collection. In conclusion, the model can predict common accident types, such as left-turns with an oncoming straight-ahead driver. In contrast, the model fails to predict rare accident types, such as left-turns (with a lit arrow sign) with oncoming traffic. Future studies should focus on optimizing the developed model, handling data imbalances, and building models using police accident data from other countries.

Scenario-based testing will help to validate automated driving systems (ADS) and establish safer road traffic. To date, most data-driven test scenario generation methods rely primarily on one data source such as police accident data (PD), naturalistic driving studies, or video-based traffic observations (VOs). However, none of these data sources perfectly satisfies all the layers of the six-layer model for the description of test scenarios. Moreover, not all available data sources cover the same location and period of time. Therefore, we fused information from 1,648 scenarios extracted from a German VO with information from 74 scenarios extracted from German PD into a comprehensive new PD* database. Finally, PD* consisted of 74 accident scenarios extended, for example, by variables containing the dynamic information of the VO scenarios. Thus, PD* contained more than 350 variables, whereas PD contained only 269 and VO only 122 variables. For fusion, we followed the Find-Unify-Synthesize-Evaluation (FUSE) for Representativity (FUSE4Rep) process model using statistical matching. Subsequently, we derived three logical scenarios from PD* to test an autonomous emergency braking system (AEB) in a stochastic traffic simulation incorporating driver-behavior models. The quality of the fusion itself was satisfactory, and we propose improving the VO data collection process and observation time to obtain even better results.

The emergence of autonomous driving represents a pivotal milestone in the evolution of the transportation system, integrating seamlessly into the daily lives of individuals due to its array of advantages over conventional vehicles. However, self-driving cars pose numerous challenges contributing to accidents and injuries annually. This paper aims to comprehensively examine the limitations inherent in autonomous driving and their consequential impact on accidents and collisions. Using data from the DMV, NMVCCS, and NHTSA, the paper reveals the key factors behind self-driving car accidents. It delves into prevalent limitations faced by self-driving cars, encompassing issues like adverse weather conditions, susceptibility to hacking, data security concerns, technological efficacy, testing and validation intricacies, information handling, and connectivity glitches. By meticulously analyzing reported accidents involving self-driving cars during the period spanning 2019 to 2022, the research evaluates statistical data pertaining to fatalities and injuries across diverse accident classifications. Additionally, the paper delves into the ethical and regulatory dimensions associated with autonomous driving, accentuating the legal complexities that arise from accidents involving self-driving vehicles. This review assists researchers and professionals by identifying current autonomous driving limitations and offering insights for safer adoption. Addressing these limitations through research can transform transportation systems for the better.

The whole of society is built on standards serving different purposes. The International Standards Organization (ISO) defines a standard as “a document that provides requirements, specifications, guidelines or characteristics that can be used consistently to ensure that materials, products, processes and services are fit for their purpose.” The National Institute of Standards and Technology, part of the U.S. Department of Commerce, elaborates further: “Standards allow technology to work seamlessly and establish trust so that markets can operate smoothly. They provide a common language to measure and evaluate performance, make interoperability of components made by different companies possible, and protect consumers by ensuring safety, durability, and market equity.” In summary, products developed according to standards protect the health and safety of end users, at the same time ensuring environmental protection. Further, standards foster competition, and end users can select from a range of interoperable products because it is what standards do. [ABSTRACT FROM AUTHOR]

The safety inspection system for the underside of vehicles demands both high detection speed and accuracy, necessitating a model with small parameters and high network efficiency. To address these issues and improve the real-time performance of under-vehicle safety inspection, the YOLOv8n object-detection algorithm was enhanced, resulting in the PSP-YOLO algorithm. Firstly, the improved PSP-YOLO network introduces a P2 small-object detection head, which is particularly effective in detecting tiny objects, such as small dangerous objects under vehicles, thereby significantly enhancing the ability to detect such small targets. Secondly, the Space-to-Depth Conv (SPD-Conv) module was introduced into the backbone network, which improves the detection performance of low-quality samples and small objects while ensuring high precision and accurate localization of small targets in images. Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model’s parameters and computational load, conserving computing resources, and improving detection speed and accuracy. Experimental results demonstrate that the modified model increased mAP@0.5 by 3.9% and mAP@0.5-0.95 by 3.6% on the dataset used, reduced the number of parameters to 2.53 M, and achieved a detection speed of 104.6 f/s. Therefore, the improved YOLOv8n algorithm significantly enhances the detection performance of dangerous objects, meeting the high-precision and real-time requirements for vehicle underside safety inspections.

This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle states, an MR damper current controller that only considers the suspension deflection and deflection rate is proposed. A linear matrix inequality problem is formulated to design the current controller, with the objective of enhancing ride safety and comfort while guaranteeing vehicle stability and robustness against any road disturbance. A series of experiments demonstrates the enhanced performance of the proposed MR damper model, which exhibits greater accuracy than other state-of-the-art damper models, such as Bingham or bi-viscous. An evaluation of the vehicle behavior under two simulated road scenarios has been conducted to demonstrate the performance of the proposed output feedback MR damper-based semi-active suspension system.

This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle’s maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle’s inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle’s load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections.

We present an automatic road incident detector characterised by a low computational complexity for easy implementation in affordable devices, automatic adaptability to changes in scenery and road conditions, and automatic detection of the most common incidents (vehicles with abnormal speed, pedestrians or objects falling on the road, vehicles stopped on the shoulder, and detection of kamikaze vehicles). To achieve these goals, different tasks have been addressed: lane segmentation, identification of traffic directions, and elimination of unnecessary objects in the foreground. The proposed system has been tested on a collection of videos recorded in real scenarios with real traffic, including areas with different lighting. Self-adaptability (plug and play) to different scenarios has been tested using videos with significant scene changes. The achieved system can process a minimum of 80 video frames within the camera’s field of view, covering a distance of 400 m, all within a span of 12 s. This capability ensures that vehicles travelling at speeds of 120 km/h are seamlessly detected with more than enough margin. Additionally, our analysis has revealed a substantial improvement in incident detection with respect to previous approaches. Specifically, an increase in accuracy of 2–5% in automatic mode and 2–7% in semi-automatic mode. The proposed classifier module only needs 2.3 MBytes of GPU to carry out the inference, thus allowing implementation in low-cost devices. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into high-performance tire characteristics at handling limits. Analysis of the tire characteristics has been conducted using the data obtained from indoor test rigs and vehicle tests. The test rig utilised a flat track test platform to obtain tire forces with precise controllability and measurability. As for the vehicle tests, driving data of a professional driver was obtained for repeatability through a high-performance test vehicle equipped with a wheel force transducer (WFT) at each wheel and a Differential Global Positioning System (DGPS). This study is primarily focused on obtaining a deeper insight into vehicle control through an analysis of tire force characteristics at handling limits. The analysis of indoor test data utilised the Magic Formula to obtain an appropriate model capable of describing real-world tire characteristics. An optimisation-based friction ellipse has been proposed to describe the tire force limits in particular. The solution of the proposed optimisation problem allows for an envelope to be generated that accurately reflects the tire force limits. Additionally, a normalised friction circle model has been proposed to quantify tire friction usage based on the proposed friction ellipse, providing a methodology to monitor the friction use of each wheel. [ABSTRACT FROM AUTHOR]

In the process of driving in an underground interchange, drivers are faced with many challenges, such as being in a closed space, visual changes alternating between light and dark conditions, complex road conditions in the confluence section, and dense signage, which directly affect the safety and comfort of drivers in an underground interchange. Thus, driving simulation, building information modeling (BIM), and data mining were used to analyze the impact of underground interchange safety facilities on driving safety and comfort. Acceleration disturbance and steering wheel comfort loss values were used to assist the comfort analysis. The CART algorithm, classification decision trees, and neural networks were used for data mining, which uses a dichotomous recursive partitioning technique where multiple layers of neurons are superimposed to fit and replace very complex nonlinear mapping relationships. Ten different scenarios were designed for comparison. Multiple linear regression combined with ANOVA was used to calculate the significance of the control variables for each scenario on the evaluation index. The results show that appropriately reducing the length of the deceleration section can improve driving comfort, setting reasonable reminder signs at the merge junction can improve driving safety, and an appropriate wall color can reduce speed oscillation. This study indicates that the placement of traffic safety facilities significantly influences the safety and comfort of driving in underground interchanges. This study may provide support for the optimization of the design of underground interchange construction and internal traffic safety facilities. [ABSTRACT FROM AUTHOR]

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(1) Background: Ensuring road user safety relies on the optimal technical condition of cars, addressing both active and passive safety measures. In Poland, vehicle regulations, articulated in the Minister of Infrastructure's decree of 31 December 2002, establish technical prerequisites and necessary equipment. For this purpose, the main question was: What is the current technical condition of cars on the road in Western Poland? (2) Methods: A total of 1067 vehicles were tested, reflecting a maximum error of 3% in a population of 20 million cars. Tests were conducted at the diagnostic station from 1 October 2022 to 30 September 2023. Statistical analysis was conducted using STATISTICA software. (3) Results: Periodic technical tests yield insights into passenger car safety standards in western Poland. The application of formulated characteristics allows a comprehensive evaluation, providing valuable information on the overall safety condition of inspected vehicles. The vehicles in Poland have an average age exceeding 14 years, and their average mileage is 168,000 km. (4) Conclusions: The examination uncovered various technical defects and provided statistical interpretations, unequivocally demonstrating that these identified issues have the potential to impact traffic safety. Such studies act as a reference point for other researchers addressing the broader issue of road traffic. [ABSTRACT FROM AUTHOR]