Situation Awareness for Driver-Centric Driving Style Adaptation

Haselberger, Johann; Stuhr, Bonifaz; Schick, Bernhard; Mueller, Steffen · 2024 · arXiv

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the challenge of adapting autonomous vehicle driving styles to individual human drivers to enhance passenger trust and acceptance. While previous approaches often rely on static parameters or ego-vehicle signals like acceleration, they frequently neglect the significant influence of the external driving context. The authors propose a situation-aware driving style adaptation method that utilizes visual feature encoders to learn representations of the driving environment from camera images. These representations are used to cluster driving situations and predict specific driving behaviors, such as lateral distance to the lane center, tailored to individual drivers. The study employs a publicly available dataset comprising 1.8 million images and labeled driving behavior data from multiple drivers. The methodology involves three main components: visual feature encoding using models like ResNet or DINOv2, unsupervised clustering (K-Means) to associate situations with specific clusters, and driving behavior prediction via either statistical lookup tables or Multilayer Perceptrons (MLPs). The authors introduce the Entropy-based Cluster Specificity (ECS) metric to evaluate the quality of situation clusters. Experiments compare task-specific pretraining on fleet data against using off-the-shelf models pretrained on ImageNet1K or unsupervised foundation models. The adaptation process freezes the visual encoder and clusters trained on fleet data, updating only the behavior predictors for specific drivers. Results demonstrate that the proposed situation-aware methods significantly outperform static driving styles in predicting human behavior, with lower root-mean-square errors. Feature encoders pretrained on the specific driving dataset yielded more precise behavior modeling, whereas encoders pretrained on ImageNet1K or unsupervised data produced more specific situation clusters but suffered from performance drops due to missing task-relevant information. In iterative real-world adaptation scenarios, MLP-based predictors exhibited catastrophic forgetting, while situation-dependent statistics based on clustering could learn iteratively from continuous data streams without such degradation. The number of clusters was found to directly impact modeling accuracy, with higher cluster counts allowing for finer situation-specific adaptation. The significance of this work lies in demonstrating that visual context is crucial for accurate driving style modeling and that decoupling situation representation from behavior prediction enables efficient, manufacturer-level pretraining and vehicle-level adaptation. The findings suggest that while unsupervised foundation models offer high interpretability through specific clustering, task-specific pretraining remains superior for behavior prediction accuracy. The proposed framework provides a scalable approach for personalizing autonomous driving functions, potentially improving user acceptance by aligning vehicle behavior with individual driver habits and situational contexts.

Key finding

A situation-aware NN driving-style model with fleet-pretrained visual encoders outperforms static styles and yields meaningful situation clusters, but MLP behaviour predictors suffer catastrophic forgetting in iterative real-world adaptation.

Methodology

lab_experiment

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via discover_arxiv on 2026-05-04 (3 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• situational awareness
• distraction detection algorithms
• telematics crash prediction
• speed choice
• anticipation
• in vehicle coaching

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Methodological Resource: tool software
• Theoretical Contribution: computational model, theory or model