Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

Wang, Shunchao; Li, Zhibin; Wang, Bingtong; Ma, Jingfeng; Yu, Jingcai

doi:10.1177/03611981211070286

Cited by 8 publications

(4 citation statements)

References 45 publications

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“…where d m,n (t ) represents the relative distance between the boundaries of HV and SV at time t. The minimum relative distance can be obtained by comparing all the relative distances between the HV and SV. The relative velocity ḋ m,n (t ) between boundaries of the HV and SV can be obtained by taking the time derivative of d m,n (t ) as shown in Equation (11).…”

Section: Definition Of Performance Metricsmentioning

confidence: 99%

“…The results showed that the potential risk characteristics, such as properly controlled intersections and road obstacles, are considered important characteristics affecting road safety. In the study of risk calculation for autonomous vehicles [11], the risk between vehicles is computed using the collision risk potential field model, which modifies the collision area based on the velocity obstacle method.…”

Section: Introductionmentioning

confidence: 99%

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Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method

Zhang,

Tak

2023

IET Intelligent Trans Sys

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Scenario‐based test methods are employed to assess the safety and performance of autonomous vehicles. The analytic hierarchy process (AHP) method is a common assessment method for determining the criticality of test scenarios. However, the AHP method is subjective and less reproducible when performed by different persons, as the elements of pairwise comparison values that are directly linked to the outcome must be assigned by the person involved. This paper proposes a novel AHP method that automatically generates pairwise comparison values by optimizing the correlation between performance metrics and risk of test scenarios by simulation. Performance metrics are defined as the minimum relative distances and corresponding relative velocities between vehicles, and the risk of the test scenario is determined by the pairwise comparison values of AHP. The novel AHP method was evaluated using a cut‐in scenario. The results showed that the minimum relative distance and the risk determined by the novel AHP method achieved a better correlation coefficient of −0.96, which is better than the conventional AHP of −0.828 and Fuzzy AHP of −0.824. These results suggest that the criticality of the test scenarios determined by the novel AHP method can more accurately reflect real‐world driving environments.

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Section: Definition Of Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method

Zhang,

Tak

2023

IET Intelligent Trans Sys

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“…In ref. [32], VO is applied to deduce the collision conditions in connected and automated vehicles. However, these VO-based methods require high computation costs in crowded environments [33].…”

Section: Related Workmentioning

confidence: 99%

Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

et al. 2022

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Collision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

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“…The normal driving environment of the vehicle is often unknown, and the obstacles that appear must be discretized and polynomial curves are added to the artificial potential field to plan a path with the optimal curvature to ensure the vehicle travels [34,35]. In the speed control of the vehicle, the desired speed can be obtained by imitating the driver's behavior by adding an artificial potential field to the path planner [36][37][38]. When there is an obstacle in front of the road, the planner can control the vehicle to decelerate in time [39].…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning Algorithm for Driverless Vehicles

Pei

Zhang

2022

Mathematics

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In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve the oscillation phenomenon existing in the improved artificial potential field method planning path. This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. First, the improved artificial potential field method is combined with the RRT algorithm, and the obstacle avoidance method of the RRT algorithm is used to solve the path oscillation; The vehicle kinematics model is then established, and under the premise of ensuring the safety of the vehicle, a model predictive control (MPC) trajectory tracking controller with constraints is designed to verify whether the path planned by the combination of the two algorithms is optimal and conforms to the vehicle motion. Finally, the feasibility of the method is verified in simulation. The simulation results show that the method can effectively solve the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion.

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Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

Cited by 8 publications

References 45 publications

Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method

Risk analysis of autonomous vehicle test scenarios using a novel analytic hierarchy process method

Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

Research on Path Planning Algorithm for Driverless Vehicles

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