# Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1803.10056 · HTML: https://mlsanity.com/paper/1803.10056/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 30 of 36)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic](https://mlsanity.com/paper/2111.06318/) (2021)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving](https://mlsanity.com/paper/2405.01440/) (2024)
- [Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF](https://mlsanity.com/paper/2406.04481/) (2024)
- [Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving](https://mlsanity.com/paper/2105.10266/) (2021)
- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Randomized Adversarial Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/1905.05637/) (2019)
- [SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning](https://mlsanity.com/paper/2104.06506/) (2021)
- [End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2104.13617/) (2021)
- [Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving](https://mlsanity.com/paper/2105.06517/) (2021)
- [Tackling Real-World Autonomous Driving using Deep Reinforcement Learning](https://mlsanity.com/paper/2207.02162/) (2022)
- [How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies](https://mlsanity.com/paper/2203.08409/) (2022)
- [Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning](https://mlsanity.com/paper/2203.10949/) (2022)
- [Deep Q-Network Based Decision Making for Autonomous Driving](https://mlsanity.com/paper/2303.11634/) (2023)
- [Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions](https://mlsanity.com/paper/2402.04168/) (2024)
- [AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning](https://mlsanity.com/paper/2504.20187/) (2025)
- [Adversarial Imitation Learning via Random Search in Lane Change Decision-Making](https://mlsanity.com/paper/2109.05197/) (2021)
- [Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2110.08124/) (2021)
- [Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2112.13646/) (2021)
- [Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic](https://mlsanity.com/paper/2212.02224/) (2022)
- [DRNet: A Decision-Making Method for Autonomous Lane Changingwith Deep Reinforcement Learning](https://mlsanity.com/paper/2311.01602/) (2023)
- [Flocking and Collision Avoidance for a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning](https://mlsanity.com/paper/2101.08074/) (2021)
- [Lane Change Decision-Making through Deep Reinforcement Learning](https://mlsanity.com/paper/2112.14705/) (2021)
- [Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles](https://mlsanity.com/paper/2112.14710/) (2021)
- [Deep Q-learning: a robust control approach](https://mlsanity.com/paper/2201.08610/) (2022)
- [Autonomous Navigation of Tractor-Trailer Vehicles through Roundabout Intersections](https://mlsanity.com/paper/2401.04980/) (2024)
- [Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework](https://mlsanity.com/paper/2407.01216/) (2024)
- [Estimating Link Flows in Road Networks with Synthetic Trajectory Data Generation: Reinforcement Learning-based Approaches](https://mlsanity.com/paper/2206.12873/) (2022)

## Abstract (arXiv)

This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced.