# Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2110.05205 · HTML: https://mlsanity.com/paper/2110.05205/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 11 Oct 2021.

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

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [A Survey of End-to-End Driving: Architectures and Training Methods](https://mlsanity.com/paper/2003.06404/) (2020)
- [PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians](https://mlsanity.com/paper/1805.11833/) (2018)
- [Urban Driving with Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1811.08586/) (2018)
- [Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning](https://mlsanity.com/paper/1911.03799/) (2019)
- [Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network](https://mlsanity.com/paper/2010.13407/) (2020)
- [Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection](https://mlsanity.com/paper/2106.04561/) (2021)
- [Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network](https://mlsanity.com/paper/2105.00153/) (2021)

## Built on by (most cited first, 5 of 5)

- [Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences](https://mlsanity.com/paper/2312.09337/) (2023)
- [HDRSplat: Gaussian Splatting for High Dynamic Range 3D Scene Reconstruction from Raw Images](https://mlsanity.com/paper/2407.16503/) (2024)
- [A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator](https://mlsanity.com/paper/2509.08221/) (2025)
- [Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving](https://mlsanity.com/paper/2505.05223/) (2025)
- [Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving](https://mlsanity.com/paper/2603.20230/) (2026)

## Abstract (arXiv)

Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a challenging and less examined research problem. This work formulates navigation in urban environments as a multi objective reinforcement learning problem. A deep learning variant of thresholded lexicographic Q-learning is presented for autonomous navigation amongst pedestrians. The multi objective DQN agent is trained on a custom urban environment developed in CARLA simulator. The proposed method is evaluated by comparing it with a single objective DQN variant on known and unknown environments. Evaluation results show that the proposed method outperforms the single objective DQN variant with respect to all aspects.