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.

翻译：尽管高速公路直线或弯道场景下的紧急避碰研究已取得大量成果，但适用于所有道路环境的通用方法尚未得到充分探索。此外，当前多数算法未考虑紧急工况下的碰撞缓解功能——当问题无可行解时该功能至关重要。为此，我们提出一种基于模型预测控制与人工势场法的安全控制器。受线电荷启发，设计了一种新型人工势场函数作为模型预测控制器的代价函数，并将车辆动力学与执行器约束纳入优化条件。该新型势场函数能表征所有物体的几何形状，特别具备拟合路口等道路结构形态的灵活性，且可通过增加对应区域的电荷密度实现车辆特定部位的碰撞缓解。我们基于两种不同车辆模型在8类场景的192个仿真算例中验证了该方法。仿真结果表明，采用独轮车模型时，所提安全控制器的成功率比基于系统分解的HJ可达性方法提升20%，并能降低43%的预设部位碰撞概率。该方法在动力学自行车模型中也得到了进一步验证。