# Quantifying and Modeling Driving Styles in Trajectory Forecasting

arXiv: https://arxiv.org/abs/2503.04994 · HTML: https://mlsanity.com/paper/2503.04994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 20 of its 31 references that are in the map.
- First posted on arXiv 6 Mar 2025.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 20 of 20)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [Just Train Twice: Improving Group Robustness without Training Group Information](https://mlsanity.com/paper/2107.09044/) (2021)
- [Wayformer: Motion Forecasting via Simple & Efficient Attention Networks](https://mlsanity.com/paper/2207.05844/) (2022)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving](https://mlsanity.com/paper/2301.08595/) (2023)
- [MSN: Multi-Style Network for Trajectory Prediction](https://mlsanity.com/paper/2107.00932/) (2021)
- [StylePredict: Machine Theory of Mind for Human Driver Behavior From Trajectories](https://mlsanity.com/paper/2011.04816/) (2020)
- [Identifying Driver Behaviors using Trajectory Features for Vehicle Navigation](https://mlsanity.com/paper/1803.00881/) (2018)
- [Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation](https://mlsanity.com/paper/2210.03772/) (2022)
- [Annotation-Free Group Robustness via Loss-Based Resampling](https://mlsanity.com/paper/2312.04893/) (2023)
- [Deep Stochastic Kinematic Models for Probabilistic Motion Forecasting in Traffic](https://mlsanity.com/paper/2406.01431/) (2024)
- [TRAVERSE: Traffic-Responsive Autonomous Vehicle Experience & Rare-event Simulation for Enhanced safety](https://mlsanity.com/paper/2407.09466/) (2024)
- [Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation](https://mlsanity.com/paper/2412.16750/) (2024)
- [Personalizing Driver Safety Interfaces via Driver Cognitive Factors Inference](https://mlsanity.com/paper/2402.05893/) (2024)
- [Adaptive Sensitivity Analysis for Robust Augmentation against Natural Corruptions in Image Segmentation](https://mlsanity.com/paper/2406.01425/) (2024)
- [DISC: Dataset for Analyzing Driving Styles In Simulated Crashes for Mixed Autonomy](https://mlsanity.com/paper/2502.00050/) (2025)

## Built on by (most cited first, 1 of 1)

- [Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics](https://mlsanity.com/paper/2607.18540/) (2026)

## Abstract (arXiv)

Trajectory forecasting has become a popular deep learning task due to its relevance for scenario simulation for autonomous driving. Specifically, trajectory forecasting predicts the trajectory of a short-horizon future for specific human drivers in a particular traffic scenario. Robust and accurate future predictions can enable autonomous driving planners to optimize for low-risk and predictable outcomes for human drivers around them. Although some work has been done to model driving style in planning and personalized autonomous polices, a gap exists in explicitly modeling human driving styles for trajectory forecasting of human behavior. Human driving style is most certainly a correlating factor to decision making, especially in edge-case scenarios where risk is nontrivial, as justified by the large amount of traffic psychology literature on risky driving. So far, the current real-world datasets for trajectory forecasting lack insight on the variety of represented driving styles. While the datasets may represent real-world distributions of driving styles, we posit that fringe driving style types may also be correlated with edge-case safety scenarios. In this work, we conduct analyses on existing real-world trajectory datasets for driving and dissect these works from the lens of driving styles, which is often intangible and non-standardized.