Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic Environments Through Representation-Based Shielding
This work addresses safety challenges for autonomous vehicles in complex urban traffic, though it appears incremental as it builds on existing RL methods with a novel state representation.
The paper tackled the problem of safe navigation for self-driving vehicles at unsignalized intersections with view obstructions and unpredictable traffic, achieving significant improvements in safety and energy consumption metrics while maintaining competitive travel speed.
Navigating unsignalized intersections in urban environments poses a complex challenge for self-driving vehicles, where issues such as view obstructions, unpredictable pedestrian crossings, and diverse traffic participants demand a great focus on crash prevention. In this paper, we propose a novel state representation for Reinforcement Learning (RL) agents centered around the information perceivable by an autonomous agent, enabling the safe navigation of previously uncharted road maps. Our approach surpasses several baseline models by a sig nificant margin in terms of safety and energy consumption metrics. These improvements are achieved while maintaining a competitive average travel speed. Our findings pave the way for more robust and reliable autonomous navigation strategies, promising safer and more efficient urban traffic environments.