# Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic Environments Through Representation-Based Shielding

arXiv: https://arxiv.org/abs/2407.04343 · HTML: https://mlsanity.com/paper/2407.04343/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving](https://mlsanity.com/paper/1904.07189/) (2019)
- [Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments](https://mlsanity.com/paper/1904.11483/) (2019)
- [Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection](https://mlsanity.com/paper/2106.04561/) (2021)
- [Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.06765/) (2021)

## Abstract (arXiv)

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.