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:尽管在高速公路情景下,对避免紧急碰撞的直径或弯曲道路进行了广泛的研究,但对于避免紧急碰撞进行了广泛的研究,但对于所有道路环境都可能采用的一般方法尚未进行彻底的探讨。此外,大多数目前的算法并不考虑在紧急情况下减少碰撞。由于问题可能没有可行的解决办法,这一功能至关重要。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,以安全控制器为例,使用模型预测控制器的成本功能,用线电荷作为我们模型预测控制器的成本功能。将车辆动态和动因限制设定为制约因素。新的人工潜在功能考虑所有物体的形状。特别是,我们提议的人工潜在功能具有灵活性,以适应道路结构的形状,例如交叉点。我们还可以通过增加相应地点的充电量,实现车辆特定部分的碰撞减缓。我们用两种不同的模型模拟在192个案例中测试了我们的方法。模拟结果表明,拟议的安全控制器的成功率比使用高20 %,而使用HJ的达标和系统分解的功能。特别是,我们提议的人为潜在功能具有灵活性,以单式循环模型为交路的形状。我们还可以降低43%的碰撞率。在前的模型中降低。还可能降低43%的机动率。