# Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving

arXiv: https://arxiv.org/abs/2301.00650 · HTML: https://mlsanity.com/paper/2301.00650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 30 Dec 2022.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 15 of 15)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Model-free Deep Reinforcement Learning for Urban Autonomous Driving](https://mlsanity.com/paper/1904.09503/) (2019)
- [CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving](https://mlsanity.com/paper/1807.03776/) (2018)
- [Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf restraining specifications](https://mlsanity.com/paper/1807.06333/) (2018)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making](https://mlsanity.com/paper/1804.07779/) (2018)
- [Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.11689/) (2019)
- [Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks](https://mlsanity.com/paper/1902.09355/) (2019)
- [Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving](https://mlsanity.com/paper/2102.09243/) (2021)
- [Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation](https://mlsanity.com/paper/1710.05627/) (2017)
- [Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning](https://mlsanity.com/paper/2004.04450/) (2020)
- [Towards Learning Multi-agent Negotiations via Self-Play](https://mlsanity.com/paper/2001.10208/) (2020)
- [Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning](https://mlsanity.com/paper/1910.09986/) (2019)

## Built on by (most cited first, 3 of 3)

- [Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars](https://mlsanity.com/paper/2311.12875/) (2023)
- [Quantum Artificial Intelligence: A Brief Survey](https://mlsanity.com/paper/2408.10726/) (2024)
- [HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving](https://mlsanity.com/paper/2510.07210/) (2025)

## Abstract (arXiv)

We present a novel hybrid learning method, HyLEAR, for solving the collision-free navigation problem for self-driving cars in POMDPs. HyLEAR leverages interposed learning to embed knowledge of a hybrid planner into a deep reinforcement learner to faster determine safe and comfortable driving policies. In particular, the hybrid planner combines pedestrian path prediction and risk-aware path planning with driving-behavior rule-based reasoning such that the driving policies also take into account, whenever possible, the ride comfort and a given set of driving-behavior rules. Our experimental performance analysis over the CARLA-CTS1 benchmark of critical traffic scenarios revealed that HyLEAR can significantly outperform the selected baselines in terms of safety and ride comfort.