# Autonomous Highway Driving using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1904.00035 · HTML: https://mlsanity.com/paper/1904.00035/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 22 of 22)

- [Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic](https://mlsanity.com/paper/2202.00881/) (2022)
- [Correct-by-Construction Runtime Enforcement in AI -- A Survey](https://mlsanity.com/paper/2208.14426/) (2022)
- [Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving](https://mlsanity.com/paper/2501.08096/) (2025)
- [Parameterized Decision-making with Multi-modal Perception for Autonomous Driving](https://mlsanity.com/paper/2312.11935/) (2023)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning](https://mlsanity.com/paper/2203.10949/) (2022)
- [Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning](https://mlsanity.com/paper/2401.08661/) (2023)
- [Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions](https://mlsanity.com/paper/2402.04168/) (2024)
- [Goal-constrained Sparse Reinforcement Learning for End-to-End Driving](https://mlsanity.com/paper/2103.09189/) (2021)
- [Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning](https://mlsanity.com/paper/2104.08876/) (2021)
- [Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems](https://mlsanity.com/paper/2408.15550/) (2024)
- [CARNet: A Dynamic Autoencoder for Learning Latent Dynamics in Autonomous Driving Tasks](https://mlsanity.com/paper/2205.08712/) (2022)
- [KARNet: Kalman Filter Augmented Recurrent Neural Network for Learning World Models in Autonomous Driving Tasks](https://mlsanity.com/paper/2305.14644/) (2023)
- [Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement](https://mlsanity.com/paper/2412.10822/) (2024)
- [A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective](https://mlsanity.com/paper/2503.23650/) (2025)
- [Robust AI Driving Strategy for Autonomous Vehicles](https://mlsanity.com/paper/2207.07829/) (2022)
- [Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator](https://mlsanity.com/paper/2211.10237/) (2022)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey](https://mlsanity.com/paper/2501.01886/) (2025)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning](https://mlsanity.com/paper/2309.12534/) (2023)
- [Learning to Drive Safely with Hybrid Options](https://mlsanity.com/paper/2510.24674/) (2025)

## Abstract (arXiv)

The operational space of an autonomous vehicle (AV) can be diverse and vary significantly. This may lead to a scenario that was not postulated in the design phase. Due to this, formulating a rule based decision maker for selecting maneuvers may not be ideal. Similarly, it may not be effective to design an a-priori cost function and then solve the optimal control problem in real-time. In order to address these issues and to avoid peculiar behaviors when encountering unforeseen scenario, we propose a reinforcement learning (RL) based method, where the ego car, i.e., an autonomous vehicle, learns to make decisions by directly interacting with simulated traffic. The decision maker for AV is implemented as a deep neural network providing an action choice for a given system state. In a critical application such as driving, an RL agent without explicit notion of safety may not converge or it may need extremely large number of samples before finding a reliable policy. To best address the issue, this paper incorporates reinforcement learning with an additional short horizon safety check (SC). In a critical scenario, the safety check will also provide an alternate safe action to the agent provided if it exists. This leads to two novel contributions. First, it generalizes the states that could lead to undesirable "near-misses" or "collisions ". Second, inclusion of safety check can provide a safe and stable training environment. This significantly enhances learning efficiency without inhibiting meaningful exploration to ensure safe and optimal learned behavior. We demonstrate the performance of the developed algorithm in highway driving scenario where the trained AV encounters varying traffic density in a highway setting.