# Multi-Objective Autonomous Braking System using Naturalistic Dataset

## TL;DR

This paper presents a deep reinforcement learning-based multi-objective autonomous braking system trained on a naturalistic dataset, balancing pedestrian safety and passenger comfort, with promising results in virtual simulations.

## Contribution

It introduces a novel multi-objective braking system using deep reinforcement learning trained on real-world-like data, comparing two RL methods for optimal control.

## Key findings

- Reduces negative passenger comfort impact by 50%.
- Maintains safe braking performance.
- Compares effectiveness of PPO and DDPG methods.

## Abstract

A deep reinforcement learning based multi-objective autonomous braking system is presented. The design of the system is formulated in a continuous action space and seeks to maximize both pedestrian safety and perception as well as passenger comfort. The vehicle agent is trained against a large naturalistic dataset containing pedestrian road-crossing trials in which respondents walked across a road under various traffic conditions within an interactive virtual reality environment. The policy for brake control is learned through computer simulation using two reinforcement learning methods i.e. Proximal Policy Optimization and Deep Deterministic Policy Gradient and the efficiency of each are compared. Results show that the system is able to reduce the negative influence on passenger comfort by half while maintaining safe braking operation.