Explore the vast range of titles available.

Provides an up-to-date, in-depth look at the current research, design, and implementation of cooperative vehicle safety communication protocols and technology Improving traffic safety has been a top concern for transportation agencies around the world and the focus of heavy research and development efforts sponsored by both governments and private industries. Cooperative vehicle systems—which use sensors and wireless technologies to reduce traffic accidents—can play a major role in making the world's roads safer. Vehicle Safety Communications: Protocols, Security, and Privacy describes fundamental issues in cooperative vehicle safety and recent advances in technologies for enabling cooperative vehicle safety. It gives an overview of traditional vehicle safety issues, the evolution of vehicle safety technologies, and the need for cooperative systems where vehicles work together to reduce the number of crashes or mitigate damage when crashes become unavoidable. Authored by two top industry professionals, the book: Summarizes the history and current status of 5.9 GHz Dedicated Short Range Communications (DSRC) technology and standardization, discussing key issues in applying DSRC to support cooperative vehicle safety Features an in-depth overview of on-board equipment (OBE) and roadside equipment (RSE) by describing sample designs to illustrate the key issues and potential solutions Takes on security and privacy protection requirements and challenges, including how to design privacy-preserving digital certificate management systems and how to evict misbehaving vehicles Includes coverage of vehicle-to-infrastructure (V2I) communications like intersection collision avoidance applications and vehicle-to-vehicle (V2V) communications like extended electronic brake lights and intersection movement assist Vehicle Safety Communications is ideal for anyone working in the areas of—or studying—cooperative vehicle safety and vehicle communications.

Urban Air Mobility (UAM) promises to reduce ground traffic and journey times by using electric vertical take-off and landing (eVTOL) aircraft for short, low-altitude flights, especially in urban environments. However, low-flying aircraft are at particularly high risk of collisions with wildlife, such as birds. This study builds on previous research into UAM collision avoidance systems (UAM-CAS) by implementing one such system in the BlueSky open-source air traffic simulator and evaluating its efficacy in reducing bird strikes. Several modifications were made to the original UAM-CAS framework to improve performance. Realistic UAM flight plans were developed and combined with real-world bird movement datasets representing typical birds in sustained flight from all seasons, recorded by an avian radar at Leeuwarden Air Base. Fast-time simulations were conducted in the BlueSky Open Air Traffic Simulator using the UAM flight plan, the bird datasets, and the UAM-CAS algorithm. Results demonstrated that, under modelling assumptions, the UAM-CAS reduced bird strikes by 62%, with an average delay per flight of 15 s, whereas 27% of the remaining strikes occurred with birds outside the system’s design scope. A small number of flights faced substantially longer delays, indicating some operational impacts. Based on the findings, specific avenues for future research to improve UAM-CAS performance are suggested.

Vehicle collision avoidance system is a kind of auxiliary driving system based on vehicle active safety, which can assist the driver to take the initiative to avoid obstacles under certain conditions, so as to effectively improve the driving safety of vehicle. This paper presents a collision avoidance system for an autonomous vehicle based on an active front steering, which mainly consists of a path planner and a robust tracking controller. A path planner is designed based on polynomial parameterization optimized by simulated annealing algorithm, which plans an evasive trajectory to bypass the obstacle and avoid crashes. The dynamic models of the AFS system, vehicle as well as the driver model are established, and based on these, a robust tracking controller is proposed, which controls the system to resist external disturbances and work in accordance with the planning trajectory. The proposed collision avoidance system is testified through CarSim and Simulink combined simulation platform. The simulation results show that it can effectively track the planning trajectory, and improve the steering stability and anti-interference performance of the vehicle.

PurposeAdvanced Driver Assistance Systems (ADAS) have been among the key innovations in the automotive market for over a decade, since they promote traffic safety. This tendency is strengthened even more lately, with the introduction of the autonomous vehicles. A plethora of ADAS exist in the market today, using common warning thresholds for all drivers. However, since we are not all driving the same way, by offering common systems for all the drivers, neither the acceptance nor the effectiveness levels of ADAS are optimal. This manuscript attempts to optimize the Collision Avoidance System (CAS) warning, through intelligent personalized algorithms.MethodsStarting with the identification of the dynamic parameters for driving behaviour modeling on the longitudinal road axis, the personalization parameters for ADAS are derived that form the basis for the algorithms developed. Also, based on literature studies, the safety boundaries for warning provision by the CAS are set and implemented in the algorithms.ResultsSpecific personalized algorithms for the longitudinal road axis behaviour are developed, based on Time to Collision and Time Headway. The proposed algorithms based on Time Headway were assessed on-road with 10 drivers and were positively evaluated by the majority of the participants, with a varying degree of reliability and usability.ConclusionsBased on the results obtained, it can be concluded that with the proposed algorithms, the initial hypothesis of the paper is verified, i.e. personalised warnings would get a greater acceptance by the drivers, of course without braking the safety limits. Further improvements of the algorithm could be achieved, possibly through a better determination of the car following event, since its definition includes a few assumptions.

This paper presents a theoretical expansion of a new intelligent algorithm called extended support vector data description (E-SVDD) for the analysis and control of dynamic groups to realize macroscopic and microscopic behavior prediction in an automotive collision avoidance system. The time to collision concept was extracted as a key parameter via system modeling and used with the E-SVDD algorithm to set up the relevant generalized theoretical system. A new method, along with its practical application, to predict the behavior of micro- and macro-systems in real time and improve the control logic for collision avoidance was realized. A numerical simulation based on actual driving data was performed to compare the proposed collision avoidance logic and the conventional one. The results confirmed the improved performance and effectiveness of the proposed control logic.

Recent reports show that the secondary collision on the road gives much higher fatality rate than the other traffic accidents. Many studies have been carried out to prevent the secondary accidents and as a result automotive companies began to introduce brake-based secondary collision avoidance systems. To prevent the secondary accidents it is important to monitor and control the lateral deviation of the vehicle after the primary collision. An estimator for the vehicle’s lateral offset and drift angle based on the in-vehicle sensors and the camera was developed in this paper. By employing sensor fusion scheme and applying extended Kalman filter, the estimator has been designed so that it works even when the camera loses the image of the lanes due to sudden change of the vehicle’s heading angle. For validation of the estimator, simulation has been carried out on various collision scenarios. The simulation results indicated that the estimator of this paper could calculate the vehicle’s lateral deviation with robustness that may be required for application in the secondary collision avoidance systems.

Driver Readiness (DR) refers to the likelihood of drivers successfully recovering control from automated driving and is correlated with collision avoidance. When designing Driver Monitoring Systems (DMS) it is useful to understand how driver states and DR interact, through predictive modelling of collision probability. However, collisions are rare and generate imbalanced datasets. Whilst rebalancing can improve model stability, reliability of correction methods remains untested in automotive research. Furthermore, it is not yet clear the extent to which certain features of driver state are associated with the probability of a collision during critical scenarios. The current study therefore had two general aims. The first was to examine statistical model reliability when using imbalance-corrected datasets; the second was to investigate the predictive utility of gaze entropy and pupil diameter in assessing collision risk during critical transitions of control from a simulated hands-off SAE L2 driving experiment. Dataset rebalancing reduced prediction accuracy and overestimated collision probabilities, aligning with prior findings on its limitations. Erratic, spatially distributed gaze fixations were associated with higher collision probability, whilst increased mental workload (indexed via mean pupil diameter) had minimal impacts. We discuss why in many situations researchers should be wary of rebalancing their datasets, and underscore gaze behaviour’s importance in DR estimation and the challenges of dataset rebalancing for predictive DR modelling.

Advanced Driver Assistance Systems (ADAS) and Collision Avoidance Systems (CAS) are the primary modules of modern human-centric and autonomous driving applications, such as forward and rear-end collision warnings. To enhance the performance of ADAS and CAS systems in foggy weather, an Efficient and Cost-Effective Vehicle Detection and Tracking with Driver Assistance (ECE-VDTDA) system is proposed. The proposed ECE-VDTDA system comprises vehicle detection, tracking, and driver assistance modules. An optimized SimYOLO-V5s_WIOU vehicle detection algorithm is proposed, based on the SimSPPF module, the baseline You Only Look Once (YOLO) algorithm (YOLO-V5s), and the Wise Intersection Over Union (WIOU) localization loss function. State-of-the-art Deep-SORT, Strong-SORT, and optimized Deep-SORT algorithms are utilized for vehicle tracking. The vehicle detection and tracking performance of the ECE-VDTDA system is rigorously evaluated on DAWN, foggy driving, foggy cityscapes, BDD100K, web-collected, and self-collected foggy weather datasets. Optimized SimYOLO-V5s_WIOU algorithm outperformed on the foggy driving dataset with a 17.45% increase in mAP50, and foggy cityscapes dataset with a 0.32%, 1.05%, 1.58%, 2%, 0.54% increase in the multiclass mAP50, mAP50-95, F1 score, precision, and recall scores, respectively, compared to the baseline YOLO-V5s. Furthermore, the SimYOLO-V5s_WIOU algorithm also outperformed the state-of-the-art methods and enables Deep-SORT, Strong-SORT, and optimized Deep-SORT vehicle tracking algorithms to track vehicles with high confidence. The driver assistance module of the ECE-VDTDA system helps prevent imminent road collisions in foggy weather by estimating distance, speed, and time-to-collision and by issuing timely collision warnings. The experimental results demonstrate the robustness and computational efficiency of the proposed ECE-VDTDA system.

Intelligent Transportation System (ITS) is continuously evolving alongside communication technologies and autonomous driving, giving way to new applications and services. Considering the significant rise in traffic casualties, protecting vulnerable road users (VRU), such as pedestrians, cyclists, motorcycles, animals, etc., has become ever more critical. That said, technological advances alone can not meet the requirements of such crucial applications. Therefore, combining them with architectural revolutions, particularly cloud, fog, and edge computing, is essential. In this review, we scrutinize the VRU safety application with regard to technological evolution. This review establishes the foundations for designing resilient, more reliable, end-to-end VRU protection services. It illustrates the possibility of combining the performance of different technologies through exploiting 5G architectural advantages (function placement, direct/indirect communication, etc.) for the intended application. In the context of 5G architecture, collision avoidance systems consider network and application-related challenges and solutions. This survey provides standardization, studies, and project efforts related to the use case and considers the different types of messages in the V2VRU communication-based safety application. We investigate how adapting the application parameters to the network state and devices’ available resources can use network resources efficiently and provide reliable services.

This paper studies the risk of ship collision off the coast of Portugal based on Automatic Identification System (AIS) data, which is recorded and maintained by the Portuguese coastal Vessel Traffic Service (VTS) control centre (CCTMC). Computer programs for decoding, visualization and analysis of the AIS data have been developed. From analysis of the AIS data available, maritime traffic off the coast of Portugal is characterized and a statistical analysis of traffic in the Traffic Separation Schemes is provided. An algorithm has been developed to assess the risk profile and the relative importance of routes associated with ports. A method is proposed to calculate the collision risk from the assessment of the number of collision candidates by estimating future distances between ships based on their previous positions, courses and speeds, and comparing those distances with a defined collision diameter. Values of causation probability suggested in several studies are used to calculate the expected number of collisions in the period of time under investigation based on the number of collision candidates. The results of this study are then compared with the number of collisions that have occurred between 1997–2006, registered and maintained by the Portuguese Maritime Authority.