论文标题

使用模型预测控制和人造潜在功能,避免紧急碰撞和缓解措施

Emergency Collision Avoidance and Mitigation Using Model Predictive Control and Artificial Potential Function

论文作者

Shang, Xu, Eskandarian, Azim

论文摘要

尽管在高速公路方案中已经针对直线或弯曲的道路进行了大量的紧急碰撞避免研究,但可以在所有道路环境中实施的一般方法尚未得到彻底探索。此外,大多数当前算法在紧急情况下不考虑减轻碰撞。此功能至关重要,因为该问题可能没有可行的解决方案。我们建议使用模型预测控制和人为潜在功能来解决这些问题的安全控制器。提出了一种新的受线电荷启发的人造潜在函数,作为我们的模型预测控制器的成本函数。车辆动力学和执行器限制被设置为约束。新的人造潜在功能考虑了所有对象的形状。特别是,我们提出的人造潜力功能具有适合道路结构(例如交叉路口)形状的灵活性。我们还可以通过增加相应位置的充电数量来实现车辆特定部分的碰撞缓解措施。我们在192个情况下,通过两种不同模型的模拟中的8种不同场景测试了我们的方法。仿真结果表明,所提出的安全控制器的成功率比使用Unicycle模型使用HJ-RECH性能与系统分解高20%。它还可以减少在预分配部分发生的碰撞的43%。该方法在动态自行车模型中得到了进一步验证。

Although extensive research in emergency collision avoidance has been carried out for straight or curved roads in a highway scenario, a general method that could be implemented for all road environments has not been thoroughly explored. Moreover, most current algorithms don't consider collision mitigation in an emergency. This functionality is essential since the problem may have no feasible solution. We propose a safe controller using model predictive control and artificial potential function to address these problems. A new artificial potential function inspired by line charge is proposed as the cost function for our model predictive controller. The vehicle dynamics and actuator limitations are set as constraints. The new artificial potential function considers the shape of all objects. In particular, the artificial potential function we proposed has the flexibility to fit the shape of the road structures, such as the intersection. We could also realize collision mitigation for a specific part of the vehicle by increasing the charge quantity at the corresponding place. We have tested our methods in 192 cases from 8 different scenarios in simulation with two different models. The simulation results show that the success rate of the proposed safe controller is 20% higher than using HJ-reachability with system decomposition by using a unicycle model. It could also decrease 43% of collision that happens at the pre-assigned part. The method is further validated in a dynamic bicycle model.

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