# Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2007.08691 · HTML: https://mlsanity.com/paper/2007.08691/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning](https://mlsanity.com/paper/2401.08661/) (2023)
- [Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning](https://mlsanity.com/paper/2310.08331/) (2023)
- [Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey](https://mlsanity.com/paper/2501.01886/) (2025)
- [I came, I saw, I certified: some perspectives on the safety assurance of cyber-physical systems](https://mlsanity.com/paper/2401.16633/) (2024)
- [Multi-Task Lane-Free Driving Strategy for Connected and Automated Vehicles: A Multi-Agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2406.14766/) (2024)
- [Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety](https://mlsanity.com/paper/2604.13878/) (2026)

## Abstract (arXiv)

Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making policy is constructed for autonomous vehicles to address the overtaking behaviors on the highway. First, a highway driving environment is founded, wherein the ego vehicle aims to pass through the surrounding vehicles with an efficient and safe maneuver. A hierarchical control framework is presented to control these vehicles, which indicates the upper-level manages the driving decisions, and the lower-level cares about the supervision of vehicle speed and acceleration. Then, the particular DRL method named dueling deep Q-network (DDQN) algorithm is applied to derive the highway decision-making strategy. The exhaustive calculative procedures of deep Q-network and DDQN algorithms are discussed and compared. Finally, a series of estimation simulation experiments are conducted to evaluate the effectiveness of the proposed highway decision-making policy. The advantages of the proposed framework in convergence rate and control performance are illuminated. Simulation results reveal that the DDQN-based overtaking policy could accomplish highway driving tasks efficiently and safely.