# Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic

arXiv: https://arxiv.org/abs/1902.05772 · HTML: https://mlsanity.com/paper/1902.05772/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2019.

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

- [Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections](https://mlsanity.com/paper/2201.07833/) (2022)
- [Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform](https://mlsanity.com/paper/2201.09463/) (2022)
- [Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions](https://mlsanity.com/paper/2402.04168/) (2024)
- [Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection](https://mlsanity.com/paper/2503.19690/) (2025)
- [Dynamic and Systematic Survey of Deep Learning Approaches for Driving Behavior Analysis](https://mlsanity.com/paper/2109.08996/) (2021)

## Abstract (arXiv)

High-level driving behavior decision-making is an open-challenging problem for connected vehicle technology, especially in heterogeneous traffic scenarios. In this paper, a deep reinforcement learning based high-level driving behavior decision-making approach is proposed for connected vehicle in heterogeneous traffic situations. The model is composed of three main parts: a data preprocessor that maps hybrid data into a data format called hyper-grid matrix, a two-stream deep neural network that extracts the hidden features, and a deep reinforcement learning network that learns the optimal policy. Moreover, a simulation environment, which includes different heterogeneous traffic scenarios, is built to train and test the proposed method. The results demonstrate that the model has the capability to learn the optimal high-level driving policy such as driving fast through heterogeneous traffic without unnecessary lane changes. Furthermore, two separate models are used to compare with the proposed model, and the performances are analyzed in detail.