# Situation Awareness for Driver-Centric Driving Style Adaptation

arXiv: https://arxiv.org/abs/2403.19595 · HTML: https://mlsanity.com/paper/2403.19595/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 166 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [How Well Do Self-Supervised Models Transfer?](https://mlsanity.com/paper/2011.13377/) (2020)
- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [What makes instance discrimination good for transfer learning?](https://mlsanity.com/paper/2006.06606/) (2020)
- [High Fidelity Visualization of What Your Self-Supervised Representation Knows About](https://mlsanity.com/paper/2112.09164/) (2021)
- [Concept Generalization in Visual Representation Learning](https://mlsanity.com/paper/2012.05649/) (2020)
- [MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving](https://mlsanity.com/paper/2301.08595/) (2023)
- [Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles](https://mlsanity.com/paper/2001.03908/) (2020)
- [Characterizing Driving Styles with Deep Learning](https://mlsanity.com/paper/1607.03611/) (2016)
- [EgoBlur: Responsible Innovation in Aria](https://mlsanity.com/paper/2308.13093/) (2023)
- [Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification](https://mlsanity.com/paper/2102.05843/) (2021)
- [Situational Awareness, Drivers Trust in Automated Driving Systems and Secondary Task Performance](https://mlsanity.com/paper/1903.05251/) (2019)
- [Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior](https://mlsanity.com/paper/2006.09267/) (2020)
- [Don't miss the Mismatch: Investigating the Objective Function Mismatch for Unsupervised Representation Learning](https://mlsanity.com/paper/2009.02383/) (2020)
- [Analysis of Truck Driver Behavior to Design Different Lane Change Styles in Automated Driving](https://mlsanity.com/paper/2012.15164/) (2020)
- [Towards Driving Policies with Personality: Modeling Behavior and Style in Risky Scenarios via Data Collection in Virtual Reality](https://mlsanity.com/paper/2303.04901/) (2023)
- [Self-Perception Versus Objective Driving Behavior: Subject Study of Lateral Vehicle Guidance](https://mlsanity.com/paper/2402.13104/) (2024)
- [Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study](https://mlsanity.com/paper/2402.14432/) (2024)

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

- [Agentic Knowledgeable Self-awareness](https://mlsanity.com/paper/2504.03553/) (2025)

## Abstract (arXiv)

There is evidence that the driving style of an autonomous vehicle is important to increase the acceptance and trust of the passengers. The driving situation has been found to have a significant influence on human driving behavior. However, current driving style models only partially incorporate driving environment information, limiting the alignment between an agent and the given situation. Therefore, we propose a situation-aware driving style model based on different visual feature encoders pretrained on fleet data, as well as driving behavior predictors, which are adapted to the driving style of a specific driver. Our experiments show that the proposed method outperforms static driving styles significantly and forms plausible situation clusters. Furthermore, we found that feature encoders pretrained on our dataset lead to more precise driving behavior modeling. In contrast, feature encoders pretrained supervised and unsupervised on different data sources lead to more specific situation clusters, which can be utilized to constrain and control the driving style adaptation for specific situations. Moreover, in a real-world setting, where driving style adaptation is happening iteratively, we found the MLP-based behavior predictors achieve good performance initially but suffer from catastrophic forgetting. In contrast, behavior predictors based on situationdependent statistics can learn iteratively from continuous data streams by design. Overall, our experiments show that important information for driving behavior prediction is contained within the visual feature encoder. The dataset is publicly available at huggingface.co/datasets/jHaselberger/SADC-Situation-Awareness-for-Driver-Centric-Driving-Style-Adaptation.