How to Brake? Ethical Emergency Braking with Deep Reinforcement Learning

TL;DR

This paper explores how deep reinforcement learning combined with vehicle communication can ethically optimize emergency braking in multi-vehicle scenarios, aiming to reduce collective harm and improve safety.

Contribution

It introduces a hybrid method combining DRL with analytical expressions to enhance reliability and performance in emergency braking decisions for connected vehicles.

Findings

Hybrid approach outperforms standalone DRL in safety metrics

Improved collision avoidance in multi-vehicle scenarios

Enhanced reliability of emergency braking decisions

Abstract

Connected and automated vehicles (CAVs) have the potential to enhance driving safety, for example by enabling safe vehicle following and more efficient traffic scheduling. For such future deployments, safety requirements should be addressed, where the primary such are avoidance of vehicle collisions and substantial mitigating of harm when collisions are unavoidable. However, conservative worst-case-based control strategies come at the price of reduced flexibility and may compromise overall performance. In light of this, we investigate how Deep Reinforcement Learning (DRL) can be leveraged to improve safety in multi-vehicle-following scenarios involving emergency braking. Specifically, we investigate how DRL with vehicle-to-vehicle communication can be used to ethically select an emergency breaking profile in scenarios where overall, or collective, three-vehicle harm reduction or collision avoidance shall be obtained instead of single-vehicle such. As an algorithm, we provide a hybrid approach that combines DRL with a previously published method based on analytical expressions for selecting optimal constant deceleration. By combining DRL with the previous method, the proposed hybrid approach increases the reliability compared to standalone DRL, while achieving superior performance in terms of overall harm reduction and collision avoidance.