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This paper focuses on the mobile robot path planning problem of optimizing the performance metrics of bidirectional A* algorithm in randomized two-dimensional map environments. An algorithm called direction constraints adaptive extended bidirectional A* (DCAE-BA*), which is an improvement of the traditional target dynamic bidirectional A* algorithm (TTD-BA*), is proposed to improve the performance metrics of the algorithm. Regarding the improvement, we propose the adaptive extension method and the direction-constrained optimal node extension method (DCONE). Simulation experiments were conducted for DCAE-BA*, TTD-BA* and traditional A* algorithm (A*) in a large number of random two-dimensional map environments. The simulation experimental scenarios consider four types of start and end point relative directions and three obstacle proportions to objectively and comprehensively evaluate the performance of the proposed algorithms. The results show that different scenarios have a significant impact on the algorithm performance metrics. Finally, the overall performance of the proposed algorithm is evaluated with a large number of experiments in "random" scenarios, and the results show that DCAE-BA* obtains significantly better search time for all three obstacle proportions, and better path length and number of expanded nodes for 10% and 25% obstacle proportions. The effectiveness of the proposed DCAE-BA* algorithm is demonstrated, which provides an essential reference for the path planning of mobile robots in a random 2D map environment. • DCAE-BA* is improved by adaptive extension and direction-constrained optimal node extension (DCONE). • The Start-End Dir and obstacle proportions have a significant impact on the algorithm metrics. • Overall, the DCAE-BA* has the highest computational efficiency for all three types of obstacle proportion. • DCAE-BA* optimizes the path length in the "diagonal" scenario most significantly. • As the proportion of obstacles increases, the metric optimization effect decreases. [ABSTRACT FROM AUTHOR]

Collision avoidance is one of the most important requirements for autonomous vehicles, particularly in complex and congested traffic scenarios where trajectories have little safety redundancy. However, simultaneously reaching the required accuracy and universal feasibility for different collision-avoidance behaviours is difficult due to the multi-state coupled motion of vehicles. To achieve the maximum traversability and ensure the safety of autonomous vehicles in any complex scenarios, we propose a quasi-critical collision-avoidance strategy based on a newly developed algorithm: the exclusive area-of-relative-velocity vector. This strategy first involves the construction of an exclusive area-of-velocity vector for each object vehicle to extract its position relative to the subject vehicle. In this procedure, to establish a subject-motion-decoupled scenario, projective transformation is applied to regularise the moving elliptical contour of the subject vehicle as a settled circle while retaining all positional relationships between the subject and object vehicles using the invariants. Subsequently, a group of escaping conditions for this exclusive area are established to express this quasi-critical collision-avoidance strategy explicitly and mathematically. The ultimate ability to escape from such an area is determined through theoretical derivations and experiments according to vehicle dynamics. In terms of real scenario data, a set of escaping equations is established to calculate the escaping conditions subject to the current state and the ultimate motion ability. Via scenario verifications, this strategy is shown to represent the safety boundary accurately and ensure quasi-critical collision-avoidance conditions under complex scenarios. • The multi-state coupled motion of self-driving vehicles can be effectively decoupled while retaining their positional relationships using the motion-decoupled extraction method proposed in this study. • The exclusive area of relative velocity vector algorithm proposed in this study produces trajectories that are free of potential collisions and available for tracking. • We propose a collision avoidance strategy with equivalence, apriority, behaviour universality, and spatio-temporal uniformity for autonomous vehicles in complex traffic condition. [ABSTRACT FROM AUTHOR]