# Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving

arXiv: https://arxiv.org/abs/2005.11470 · HTML: https://mlsanity.com/paper/2005.11470/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2020.

## Built on by (most cited first, 12 of 12)

- [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments](https://mlsanity.com/paper/2207.09934/) (2022)
- [Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2509.10570/) (2025)
- [Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks](https://mlsanity.com/paper/2308.07439/) (2023)
- [Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning](https://mlsanity.com/paper/2405.07543/) (2024)
- [Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation](https://mlsanity.com/paper/2404.17820/) (2024)
- [Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling](https://mlsanity.com/paper/2212.02908/) (2022)
- [Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework](https://mlsanity.com/paper/2404.14713/) (2024)
- [Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving](https://mlsanity.com/paper/2502.21134/) (2025)
- [Deep Fictitious Play-Based Potential Differential Games for Learning Human-Like Interaction at Unsignalized Intersections](https://mlsanity.com/paper/2506.12283/) (2025)
- [Self-Interest and Systemic Benefits: Emergence of Collective Rationality in Mixed Autonomy Traffic Through Deep Reinforcement Learning](https://mlsanity.com/paper/2511.04883/) (2025)

## Abstract (arXiv)

Driving in a human-like manner is important for an autonomous vehicle to be a smart and predictable traffic participant. To achieve this goal, parameters of the motion planning module should be carefully tuned, which needs great effort and expert knowledge. In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed. The learning is achieved by encouraging the selected trajectory to approximate the human driving trajectory under the same traffic situation. The employed motion planner follows a widely accepted methodology that first samples candidate trajectories in the trajectory space, then select the one with minimal cost as the planned trajectory. Moreover, in addition to traditional factors such as comfort, efficiency and safety, the cost function is proposed to incorporate incentive of behavior decision like a human driver, so that both lane change decision and motion planning are coupled into one framework. Two types of lane incentive cost -- heuristic and learning based -- are proposed and implemented. To verify the validity of the proposed method, a data set is developed by using the naturalistic trajectory data of human drivers collected on the motorways in Beijing, containing samples of lane changes to the left and right lanes, and car followings. Experiments are conducted with respect to both lane change decision and motion planning, and promising results are achieved.