Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving

01/20/2022
by   Zehong Cao, et al.
0

Autonomous driving has been at the forefront of public interest, and a pivotal debate to widespread concerns is safety in the transportation system. Deep reinforcement learning (DRL) has been applied to autonomous driving to provide solutions for obstacle avoidance. However, in a road traffic junction scenario, the vehicle typically receives partial observations from the transportation environment, while DRL needs to rely on long-term rewards to train a reliable model by maximising the cumulative rewards, which may take the risk when exploring new actions and returning either a positive reward or a penalty in the case of collisions. Although safety concerns are usually considered in the design of a reward function, they are not fully considered as the critical metric to directly evaluate the effectiveness of DRL algorithms in autonomous driving. In this study, we evaluated the safety performance of three baseline DRL models (DQN, A2C, and PPO) and proposed a self-awareness module from an attention mechanism for DRL to improve the safety evaluation for an anomalous vehicle in a complex road traffic junction environment, such as intersection and roundabout scenarios, based on four metrics: collision rate, success rate, freezing rate, and total reward. Our two experimental results in the training and testing phases revealed the baseline DRL with poor safety performance, while our proposed self-awareness attention-DQN can significantly improve the safety performance in intersection and roundabout scenarios.

READ FULL TEXT

page 1

page 4

page 6

page 7

page 8

page 9

page 10

research
07/03/2023

Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach

The dynamic nature of driving environments and the presence of diverse r...
research
05/05/2021

Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance

The separation assurance task will be extremely challenging for air traf...
research
06/20/2023

Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning

Traffic scenarios in roundabouts pose substantial complexity for automat...
research
09/07/2017

Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge

Multiple automakers have in development or in production automated drivi...
research
11/22/2022

Don't Watch Me: A Spatio-Temporal Trojan Attack on Deep-Reinforcement-Learning-Augment Autonomous Driving

Deep reinforcement learning (DRL) is one of the most popular algorithms ...
research
05/26/2023

Physical Deep Reinforcement Learning: Safety and Unknown Unknowns

In this paper, we propose the Phy-DRL: a physics-model-regulated deep re...
research
01/06/2021

A Survey of Deep RL and IL for Autonomous Driving Policy Learning

Autonomous driving (AD) agents generate driving policies based on online...

Please sign up or login with your details

Forgot password? Click here to reset