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Compared with automatic collision avoidance systems, collision avoidance assistance systems have attracted more research interest because they help avoid collisions in near-accident situations. However, ensuring the robustness and reliability of collision avoidance assistances is difficult because of problems in reliable environment recognition, ac...
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... Upon detecting a collision risk, the automatic controller compares the expected and actual vehicle paths, evaluates the accuracy of braking and steering, and activates avoidance interventions, including brake trigger collision, active front steering (AFS), electronic power steering (EPS), four-wheel steering (4WS), yaw moment control (YMC), and electronic stability control (ESC) [12]. This research on CAS in i-EFV holds particular significance [13]. ...
In this study, a novel approach is presented that integrates braking and active emergency steering control for vehicles to prevent or mitigate side and rear-end collisions. A T-type active emergency steering strategy is proposed, designed to avoid both side and rear-end collisions at intersections. To ensure vehicle stability and minimize steering time during the T-type active emergency steering process, a nonlinear dynamic model for the vehicle and a nonlinear tire model are developed. The constraints of the starting and ending states of the steering process are also analyzed. This problem is formulated as an optimization control problem with boundary value constraints. The Radau pseudospectral method is employed to solve the optimized anti-collision control problem. Simulation results demonstrate that timely initiation of the anti-collision strategy can successfully prevent collisions. The study outlines the application's conditions and principles, which hold significant potential for enhancing the active safety of intelligent vehicles.
... However, their implementation is a major challenge for both rule-based control and data-driven decision-making. Readers are encouraged to refer to 31,32 for the appropriate analysis of key technologies for assistance systems for collision prevention. Multiple business giants in different countries are currently working on the production of AVs. ...
Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.
... We move the detection window from 1 to 3 seconds prior to the failures (time to failure, TTF for short). Studies on pre-crash automated seat belt systems [39,81] indicate a range between 3 seconds to half a second as adequate TTF values for the activation of automated seat belt tightening. Also, according to previous studies in the Udacity simulator [59], a TTF of 3 seconds is deemed sufficient to avoid failures at 30 mph, which is the constant cruising speed of the ADS in the simulator. ...
Automated online recognition of unexpected conditions is an indispensable component of autonomous vehicles to ensure safety even in unknown and uncertain situations. In this paper we propose a runtime monitoring technique rooted in the attention maps computed by explainable artificial intelligence techniques. Our approach , implemented in a tool called ThirdEye, turns attention maps into confidence scores that are used to discriminate safe from unsafe driving behaviours. The intuition is that uncommon attention maps are associated with unexpected runtime conditions. In our empirical study, we evaluated the effectiveness of different configurations of ThirdEye at predicting simulation-based injected failures induced by both unknown conditions (adverse weather and lighting) and unsafe/uncertain conditions created with mutation testing. Results show that, overall, ThirdEye can predict 98% misbehaviours, up to three seconds in advance, outperforming a state-of-the-art failure predictor for autonomous vehicles.
... However, their implementation is a major challenge for both the rule-based control and data-driven decision-making science communities. Readers are encouraged to refer to 30,31 for the appropriate analysis of key technologies for assistance systems for collision prevention. Multiple business giants in different countries are currently working on the production of AVs. ...
Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward Automated Vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for Multiple Vehicle Cooperation and Collision Avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers a AI-enable conceptual framework, a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight on the benefits of the greater efficiency of AVs set-up for academics and policymakers. Finally, the open research issues discussed in this article will pave the way for the actual implementation of driver-less automated traffic systems.
... Common examples are four-wheel steering (4WS), active front steering (AFS), direct yaw-moment control (DYC), and active suspension system (ASS) [14]. These control systems ensure yaw stability based on the current vehicle state, while additional collision avoidance systems apply control to correct trajectory error within vehicle stability limits [15]. However, due to inherent nonlinearities in vehicle lateral dynamics and time-varying longitudinal speed, vehicle lateral stability depends on not only the lateral dynamic states during the steering, but also the longitudinal states before steering. ...
... ψ̇= ω y sinϕ cosθ + ω z cosϕ cosθ (13) ϕ̇= ω x + ω y sinϕtanθ + ω z cosϕtanθ (14) In the case that the vehicle is front wheel driven, the equations of front right and rear right wheel rotational dynamics are given as [35][36][37]41]: (15) J w ω̇r r = −T brr − r rr F xtrr (16) The notation used is as follows: T drf and T brf are the driving torque and braking torque applied to the right front wheel; T brr is the braking torque applied to the right rear wheel; r rf and r rr denote the effective rolling radius of front and rear tires; ω rf and ω rr denote the angular velocity of front and rear wheels. ...
Most controllers concerning lateral stability and rollover prevention for autonomous vehicles are designed separately and used simultaneously. However, roll motion influences lateral stability in cornering maneuvers, especially at high speed. Typical rollover prevention control stabilizes the vehicle with differential braking to create an understeering condition. Although this method can prevent rollover, it can also lead to deviation from a reference path specified for an autonomous vehicle. This contribution proposes and implements a coupled longitudinal and lateral controller for path tracking via model predictive control (MPC) to simultaneously enforce constraints on control input, state output, lateral stability, and rollover prevention. To demonstrate the approach in simulation, an 8 degrees of freedom (DOF) vehicle model is used as the MPC prediction model, and a high-fidelity 14-DOF model as the plant. The MPC-based lateral control generates a sequence of optimal steering angles, while a PID speed controller adjusts the driving or braking torque. The lateral stability envelope is determined by the phase plane of yaw rate and lateral velocity, while the roll angle threshold is derived from the load transfer ratio (LTR) and tire vertical force under the condition of quasi-steady-state rollover. To track the desired trajectory as fast as possible, a minimum-time velocity profile is determined using a forward-backward integration approach, subject to tire friction limit constraints. We demonstrate the approach in simulation, by having the vehicle track an arbitrary course of continuously varying curvature thus highlighting the accuracy of the controller and its ability to satisfy lateral and roll stability requirements. The MATLAB® code for the 8-DOF and 14-DOF vehicle models, along with the implementation of the proposed controller are available as open source in the public domain.
... Since the introduction of sensors is not the focus of this survey, the detailed introduction of sensor principles and characteristics can be referred to [15], [65]- [67]. ...
Accurately discovering hazards and issuing appropriate warnings to drivers in advance or performing autonomous control is the core of the Collision Avoidance (CA) system used to solve traffic safety problems. More comprehensive environmental awareness, diversified communication technologies, and autonomous control can make the CA system more accurate and effective, thereby improving driving safety. In addition, the assistance of Artificial Intelligence (AI) technology can make the CA system adapt to the environment and facilitate fast and accurate decisions. Considering the current lack of a thorough survey of driving safety with sensing, vehicular communications, and AI-based collision avoidance, in this paper, we survey existing researches for state-of-the-art data-driven CA techniques. Firstly, we discuss the major steps of CA and key research issues. For each step, we review the existing enabling techniques and research methods for CA in detail, including sensing and vehicular communication for safe driving, as well as CA algorithm design. Particularly, we present a comparison between the most common AI algorithms for different functions in the CA system. Testbeds and projects for CA are summarized next. Finally, several open challenges and future research directions are also outlined.
... Bella and Russo [18] conducted a study into rear-end collision warning system based on simulators, but this was tailored more towards driver behavior. Zhao et al. [19] produced an in-depth review of collision avoidance systems, including both sensors and communication, that details the benefits of each. ...
Dedicated Short-Range Communication (DSRC) or IEEE 802.11p/OCB (Out of the Context of a Base-station) is widely considered to be a primary technology for Vehicle-to-Vehicle (V2V) communication, and it is aimed toward increasing the safety of users on the road by sharing information between one another. The requirements of DSRC are to maintain real-time communication with low latency and high reliability. In this paper, we investigate how communication can be used to improve stopping distance performance based on fieldwork results. In addition, we assess the impacts of reduced reliability, in terms of distance independent, distance dependent and density-based consecutive packet losses. A model is developed based on empirical measurements results depending on distance, data rate, and traveling speed. With this model, it is shown that cooperative V2V communications can effectively reduce reaction time and increase safety stop distance, and highlight the importance of high reliability. The obtained results can be further used for the design of cooperative V2V-based driving and safety applications.
... With the apparition of ADAS, several studies had been performed in order to determine their potential benefits on road safety (Coelingh et al 2010;Jermakian (2011);Zhao et al. 2017). Among the different ADAS that can be found in the market, a focus will be given here on AEB and FCW. ...
In 2016, road fatalities reached 1.35 million in the world according to the World Health Organization. 26% of these fatalities were pedestrians and cyclists. Nowadays, more and more cars are equipped with an emergency system (called FCW – Forward Collision Warning) that can detect pedestrians and cyclists in order to warn drivers of a hazardous situation. These systems can also help in collision avoidance either by assisting driver during braking or by activating an Automatic Emergency Braking (AEB). This thesis is focused on Pedestrian and Cyclist AEB and FCW assessment and has three main objectives.First, an analysis on more than 3700 accident case reconstructions (2200 cyclist cases and 1500 pedestrian cases) from two databases, one French and one German has been performed. Accident configurations have been extracted and classified into different scenarios. A simulation software has been implemented in order to replay the accident kinematics with the integration of an AEB by varying their system characteristics. This allows the identification of optimum characteristics for a pedestrian AEB and cyclist AEB in terms of road user detection. It also allows identifying FCW trigger time and the duration of an emergency braking.Secondly, based on an experimental campaign using a driving simulator, the driver’s reactions to a FCW signal have been analyzed on different accident configurations: pedestrian/cyclist cases, with/without FCW and with different FCW triggers. Two hundred volunteers participated in this experiment. The results concern the gaze analysis, the driver’s response to the FCW signal, the time reaction to trigger a braking and the different behavior depending on the driving configurations.The third objective concerns the benefits assessment of a FCW. Based on the results of the driving simulator experiment and the kinematic reconstructions of the accidents, benefits of a FCW are estimated in terms of potential avoided or mitigated accidents.Finally, some perspectives of this work are proposed.
... In rightturning collision-related research, Sitao et al. put forward an intersection optimization design to reduce the collision probability between right-turning vehicles and pedestrians [11], but it cannot cover all possible right-turning collisions in intersections. Zhao et al. have conducted research in intelligent vehicles active collision avoidance related fields [12]. Choi and Zhao et al. adopted the autonomous emergency braking (AEB) system to avoid collisions [13,14]. ...
... Finally, the simulation results showed that the intelligent right-turning vehicle collision probability calculation algorithm could calculate collision probability and the two- Scenario 2: when the vehicle turned right into the lane, it was difficult to find pedestrians who were crossing the road in the lane, so it was easy to cause serious traffic accidents. e parameters in this scenario were defined as follows: pt � 0.01 s, T � 5 s, L 1 � 8 m, W 1 � 2 m, and D � 1.5 m; the pedestrian's coordinates were (8,12). V 1 , V 2 , and R consist of 10,000 normally distributed random numbers, V 1 ∼ N (14, 1), V 2 ∼ N (1, 2), and R ∼ N (20, 1); V 2 represented the velocity of the pedestrian in this case. ...
... erefore, the calculation algorithm of the collision probability curve (A curve) can be verified. (9,12). V 1 , V 2 , and R consist of 10000 normal distribution random numbers, V 1 ∼ N (12, 1), V 2 ∼ N (3, 1), and R ∼ N (20, 1). ...
With the development of intelligent vehicle technology, the demand for advanced driver assistant systems kept increasing. To improve the performance of the active safety systems, we focused on right-turning vehicle’s collision warning and avoidance. We put forward an algorithm based on Monte Carlo simulation to calculate the collision probability between the right-turning vehicle and another vehicle (or pedestrian) in intersections. We drew collision probability curves which used time-to-collision as the horizontal axis and collision probability as the vertical axis. We established a three-level collision warning system and used software to calculate and simulate the collision probability and warning process. To avoid the collision actively when turning right, a two-stage braking strategy is applied. Taking four right-turning collision conditions as examples, the two-stage braking strategy was applied, analysing and comparing the anteroposterior curve diagram simultaneously to avoid collision actively and reduce collision probability. By comparison, the collision probability 2 s before active collision avoidance was more than 80% and the collision probability may even reach 100% in certain conditions. To improve the active safety performance, the two-stage braking strategy can reduce the collision probability from exceeding 50% to approaching 0% in 2 s and reduce collision probability to less than 5% in 3 s. By changing four initial positions, the collision probability curve calculation algorithm and the two-stage braking strategy are validated and analysed. The results verified the rationality of the collision probability curve calculation algorithm and the two-stage braking strategy.
