A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance ( MVCA ) algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently, without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore, MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay ( less than 100 ms ) and low packet loss ( less than 5% ) can bring little influence to those trajectory planning algorithms that only depend on V2V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.

中文翻译：

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多车防撞算法仿真与现场测试


基于A*算法设计了智能车辆全局规划算法，为智能车辆提供到达目的地的全局路径。通过扩展相互n体防撞方法，提出了一种分布式实时多车防撞(MVCA)算法。 MVCA 使智能车辆能够独立选择目的地并控制输入，无需相互协商或与协调器协商。与集中式轨迹规划算法相比，MVCA降低了计算成本，并大大提高了系统的鲁棒性。由于每辆智能车辆的目的地都可以被视为私有，可以受到MVCA的保护，同时MVCA可以为智能车辆提供实时的轨迹规划。因此，MVCA可以更好地提高智能汽车的安全性。在MATLAB中进行仿真，包括十字路口场景仿真和圆形交换位置仿真。结果表明MVCA运行安全可靠。通过理论上制定基于一维马尔可夫链的广播过程，还统计研究了延迟和丢包对 MVCA 的影响。结果表明，容忍延迟不应超过轨迹规划决策周期的一半，缩短发送间隔可以在一定程度上减轻丢包带来的负面影响。短时延（小于100ms）和低丢包（小于5%）的情况对那些仅依靠V2V感知上下文的轨迹规划算法影响不大，但如果时延和丢包率较高，则可能会发生不可预测的碰撞。丢包情况进一步恶化。 MVCA还通过真实的智能汽车进行了测试，测试结果证明了MVCA的可操作性。