Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving

TL;DR

This paper presents a deep reinforcement learning approach for autonomous braking and throttle control, enabling safe, smooth, and human-like driving in complex multi-agent environments with obstacles and intersections.

Contribution

It introduces a continuous action space DRL system for autonomous vehicle control, demonstrating effective collision avoidance and compliance with traffic regulations in simulation.

Findings

System successfully avoids collisions in simulated scenarios.

Ensures smooth transitions in throttle and brake application.

Resembles human driving behavior in emergency and regular situations.

Abstract

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.