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Abstract This study aims to take the first step in bridging the gap between vehicle dynamics systems and autonomous control strategies research. More specifically, a nested method is employed to evaluate the collision avoidance ability of autonomous vehicles in the primary design stage theoretically based on both dynamics and control parameters. An integrated model is derived from a half car mathematical model in the lateral direction, consisting of two degrees of freedom, lateral deviation and yaw angle, with a traction mathematical model in the longitudinal direction, consisting of two degrees of freedom, the longitudinal velocity and rolling velocity of the wheel. The integrated model uses a mathematical power train model to generate the torque on the wheel and connects the two systems via the magic formula tyre model to represent the tyre non-linearity during augmented longitudinal and lateral dynamic attitudes.

Abstract Obstacle avoidance is an important function in self-driving vehicle control. When the vehicle move from any arbitrary start positions to any target positions in environment, a proper path must avoid both static obstacles and moving obstacles of arbitrary shape. There are many possible scenarios, manually tackling all possible cases will likely yield a too simplistic policy. In this paper reinforcement learning is applied to the problem to form effective strategies. There are two major challenges that make self-driving vehicle different from other robotic tasks. Firstly, in order to control the vehicle precisely, the action space must be continuous which can’t be dealt with by traditional Q-learning. Secondly, self-driving vehicle must satisfy various constraints including vehicle dynamics constraints and traffic rules constraints. Three contributions are made in this paper.

Abstract ISO 26262 needs to be considered when developing safety-relevant E/E systems within the automotive industry. One part of the development process according to ISO 26262 is the derivation of the safety requirements for component functions. Here, one attribute of the safety requirements is the Automotive Safety Integrity Level (ASIL). The ASIL at a component level can be determined using ASIL allocation and decomposition. Considering complex systems such as vehicle electrical systems, countless possibilities can be identified for how the ASILs at a component level can be assigned in line with safety goals. In terms of efficiency, manual assignment is not expedient. Therefore, an algorithm for automated assignment of the ASILs will be introduced which considers constraints based on a fault tree analysis. The function of the approach will be demonstrated using the example of a vehicle electrical system from an automated vehicle.

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract Identifying and eliminating failure scenarios is critical in the development of autonomous vehicle (AV) systems. However, finding such failures through real-world vehicle-level testing is a difficult task as system disengagements and accidents are rare occurrences. Simulation approaches have been proposed to supplement vehicle-level testing and reduce the costs associated with operating large fleets of autonomous test vehicles. While one can run more vehicles in simulation than in the real world, applying traditional Monte Carlo sampling techniques to find failures still yields an unguided search and a large waste of computing resources. A more directed method than random sampling is needed to identify failure scenarios in a computationally efficient manner. Adaptive Stress Testing (AST) is a method that uses reinforcement learning (RL) paradigms to efficiently find failure scenarios in stochastic sequential decision-making systems.

Abstract Due to their large volume structure, when a heavy vehicle encounters sudden road conditions, emergency turns, or lane changes, it is very easy for vehicle rollover accidents to occur; however, well-designed suspension systems can greatly reduce vehicle rollover occurrence. In this article, a novel semi-active suspension adaptive control based on AdaBoost algorithm is proposed to effectively improve the vehicle rollover stability under dangerous working conditions. This research first established a vehicle rollover warning model based on the AdaBoost algorithm. Meanwhile, the approximate skyhook damping suspension model is established as the reference model of the semi-active suspension. Furthermore, the model reference adaptive control (MRAC) system is established based on Lyapunov stability theory, and the adaptive controller is designed.

Abstract The development of connected-vehicle technology, which includes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, opens the door for unprecedented active safety and driver-enhanced systems. In addition to exchanging basic traffic messages among vehicles for safety applications, a significantly higher level of safety can be achieved when vehicles and designated infrastructure locations share their sensor data. In this article, we propose a new system where cameras installed on multiple vehicles and infrastructure locations share and fuse their visual data and detected objects in real time. The transmission of camera data and/or detected objects (e.g., pedestrians, vehicles, cyclists, etc.) can be accomplished by many communication methods. In particular, such communications can be accomplished using the emerging Dedicated Short-Range Communications (DSRC) technology.

Abstract It is a challenge to find a safe trajectory for automated vehicles in urban environments with pedestrians. The prediction of future movements with 100% certainty is impossible, if the intention is unknown. A Gaussian process approach is used to formulate future movement hypotheses of the pedestrian based on historical movements. A mixed integer linear programming (MILP) optimization approach is used for the trajectory planning of the vehicle. The collision probability between the ego-vehicle and pedestrian is used as constraints in the optimization. This approach is useful for cooperative vehicle systems, with historical movement data in a fixed urban environment (e.g., intersection) and the premise that pedestrians follow typical movement data.

Abstract This work was supported jointly by the National Science Foundation of China under Grant No. 51875184 and the National key R&D programs, China New energy vehicles focus on special projects under Grant No. 2016YFB0100903-2.

Abstract In order to further improve the active safety protection of the vehicle’s active collision avoidance system for vulnerable road users, consider the limitations of on-board sensors, a pedestrian active collision avoidance control strategy based on vehicle-to-vehicle (V2V) communication technology is proposed for the blind-spot dangerous scenario where pedestrians pass through the front of a stationary obstacle vehicle and collide with the host vehicle. Firstly, the relative position relationship model between the host vehicle and the pedestrian is established according to the pedestrian information detected by the obstacle vehicle sensor and the global positioning system (GPS) position information of the obstacle vehicle and the host vehicle so that the host vehicle can obtain the state information of the pedestrian in front of the obstacle vehicle through V2V communication.

Abstract Numerous operational procedures regulate aerodrome ground traffic. Detailed solutions in these procedures often come from preventive recommendations formulated as a result of accident cause analysis. With time, the conclusions drawn based on incidents, i.e., events that did not result in material damage or casualties, are becoming increasingly significant. In this article, we propose a new method for determining the probability of an incident turning into an air accident, based on the example of aerodrome traffic operations. Premises conducive to an accident in the considered class of events depend on both human and physical factors. Thus a hybrid approach was applied. We used a fuzzy inference system to analyze the premises dependent on vehicle operators, while the simulation method was selected to examine the premises dependent on physical factors. Both were integrated using the technique of event trees with fuzzy probabilities (ETFP).

Abstract The driving safety performance of autonomous driving vehicles must be ensured before on-road implementation. Because it is not realistic to evaluate every single test condition in real-world traffic, computer simulation methods can be used. The driving safety performance can be evaluated by simulating various driving scenarios and calculating surrogate indicators representing dangerous collision risk. This study used a near-miss database and introduced a surrogate indicator that represents a potential risk in the driving scenarios for rear-end and cut-in collisions. The near-miss video database includes several driving scenarios experienced by human drivers, such as dangerous situations that lead to accidents, potentially dangerous situations that have a risk probability to escalate into dangerous situations, and normal driving situations. A skilled and attentive human driver foresees dangerous situations while driving and avoids them.

Abstract Safety is always a crucial aspect of developing autonomous systems, and the motivation behind this project comes from the need to address the traffic crashes occurring globally on a daily basis. The present work studies the coexistence of the novel rule-based behavioral planning framework with the five key advanced driver assistance system (ADAS) features as proposed in this article to fulfill the safety requirements and enhance the comfort of the driver/passengers to achieve a receding-horizon autopilot. This architecture utilizes data from the sensor fusion and the prediction module for the prediction time horizon of 2 s iteratively, which is continuously moving forward (hence, the receding horizon), and helps the behavior planner understand the intent of other vehicles on the road in advance.

Abstract For safe and comfortable automated driving in the urban domain, especially in complex geometries as intersections, the prediction of surrounding traffic participants is fundamental. Several works in this field focus on predicting the behavior of vulnerable road users (VRU) at crossings. However, no approaches were found dealing with predicting the interaction between turning vehicles giving right of way or cooperating with VRU, which is substantial for the trajectory planning of following vehicles. Infrastructural sensor data from an intersection in Germany enables the development of a prediction concept for vehicles interacting with VRU. Our studies show that the original criteria for classifying an interaction between vehicles and VRU—the post-encroachment time (PET)—is not suitable as ground truth criteria for the aimed prediction. Instead, a clustering-based labelling approach with k-means shows promising results in trajectory pattern distinction.

Abstract TOC

Abstract In this article, we present a rigorous modular statistical approach for arguing the safety or its insufficiency of an autonomous vehicle through a concrete illustrative example. The methodology relies on making appropriate quantitative studies of the performance of constituent components. We explain the importance of sufficient and necessary conditions at the component level for the overall safety of the vehicle, as well as the cost-saving benefits of the approach. A simple concrete automated braking example studied illustrates how the separate perception system and Operational Design Domain (ODD) statistical analyses can be used to prove or disprove safety at the vehicle level.

Letter from the Special Issue Editors

Abstract The active anti-roll bar (AARB) system in vehicles has recently become one of the research hotspots in the field of vehicle technology to improve the vehicle’s active safety. In most off-road vehicles, high ground clearance is required while keeping all wheels in contact with the ground in order to improve traction and maintain load distribution among the wheels. A problem however arises in some types of the off-road vehicles when the vehicle is operated at high speeds on smooth roads. In such condition, the combination of the vehicle’s center of gravity position, large suspension stroke, and soft spring construction creates a stability problem, which could make the vehicle liable to rollover. This article analyzes a comparison of stability performance between passive and active anti-roll bar systems to improve rolling resistance. For active systems, two control strategies will be investigated. The conventional PID controller is firstly investigated and taken as a reference.

Abstract Collision alert and avoidance systems (CAS) could help to minimize driver errors. They are instrumental as an advanced driver-assistance system (ADAS) when the vehicle is facing potential hazards. Developing effective ADAS/CAS, which provides alerts to the driver, requires a fundamental understanding of human sensory perception and response capabilities. This research explores the premise that external stimulation can effectively improve drivers’ reaction and response capabilities. Therefore this article proposes a light-emitting diode (LED)-based driver warning system to prevent potential collisions while evaluating novel signal processing algorithms to explore the correlation between driver brain signals and external visual stimulation. When the vehicle approaches emerging obstacles or potential hazards, an LED light box flashes to warn the driver through visual stimulation to avoid the collision through braking.

Abstract Advanced passenger vehicles are complex dynamic systems that are equipped with several actuators, possibly including differential braking, active steering, and semi-active or active suspensions. The simultaneous use of several actuators for integrated vehicle motion control has been a topic of great interest in literature. To facilitate this, a technique known as control allocation (CA) has been employed. CA is a technique that enables the coordination of various actuators of a system. One of the main challenges in the study of CA has been the representation of actuator dynamics in the optimal CA problem (OCAP). Using model predictive control allocation (MPCA), this problem has been addressed. Furthermore, the actual dynamics of actuators may vary over the lifespan of the system due to factors such as wear, lack of maintenance, etc. Therefore, it is further required to compensate for any mismatches between the actual actuator parameters and those used in the OCAP.