深層強化学習による自動ブレーキ・スロットル制御：自然な運転の実現
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving
深層決定論的方策勾配法（DDPG）を用いて、連続値のブレーキ・スロットル操作を学習し、障害物回避や交差点での安全で滑らかな運転を実現するシステムを提案した。
著者: Varshit S. Dubey, Ruhshad Kasad, Karan Agrawal
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
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.