论文标题
自动制动和节气门系统：一种自然主义驾驶的深入加强学习方法
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving
论文作者
论文摘要
自动制动和油门控制是为未来开发安全驾驶系统的关键。存在自动驾驶汽车在确保安全性和舒适性的同时协商多个环境。提出了基于强化的基于学习的自主油门和制动系统。对于每个时间步，提议的系统都决定应用制动器或油门。油门和制动器被建模为连续的动作空间值。我们演示了2个场景，其中需要复杂的制动和油门系统，即当我们的代理商前面有静态障碍物，例如汽车，停车标志。第二种情况由2辆接近交叉路口的车辆组成。使用深层确定性政策梯度通过计算机模拟来学习制动和油门控制的政策。该实验表明，该系统不仅避免了碰撞，而且还确保了在避开紧急情况情况并遵守速度法规的情况下，油门/制动器的值会平稳变化，即系统类似于人类驾驶。
Autonomous Braking and Throttle control is key in developing safe driving systems for the future. There exists a need for autonomous vehicles to negotiate a multi-agent environment while ensuring safety and comfort. A Deep Reinforcement Learning based autonomous throttle and braking system is presented. For each time step, the proposed system makes a decision to apply the brake or throttle. The throttle and brake are modelled as continuous action space values. We demonstrate 2 scenarios where there is a need for a sophisticated braking and throttle system, i.e when there is a static obstacle in front of our agent like a car, stop sign. The second scenario consists of 2 vehicles approaching an intersection. The policies for brake and throttle control are learned through computer simulation using Deep deterministic policy gradients. The experiment shows that the system not only avoids a collision, but also it ensures that there is smooth change in the values of throttle/brake as it gets out of the emergency situation and abides by the speed regulations, i.e the system resembles human driving.
