Identifying Modes of Intent from Driver Behaviors in Dynamic Environments
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Summary
This paper addresses the challenge of modeling human driver intent to improve the safety and social acceptance of autonomous and semi-autonomous vehicles in heterogeneous environments. The authors argue that existing driver models often rely on heuristics or time-based windows to detect maneuvers like lane changes, failing to capture the dynamic, environment-driven nature of human decision-making. To address this, the study proposes a hybrid system formulation that identifies discrete "modes of intent" (lane keeping, preparing to lane change, and lane changing) based solely on sensor measurements of the surrounding environment, rather than driver control inputs or internal states. This approach aims to create a portable model that can mimic human flexibility and adaptability while mitigating distractions, thereby facilitating better human-robot collaboration. The methodology employs a supervised classification framework trained on a dataset collected from a realistic driving simulator. Five human subjects drove a two-lane road scenario in a motion platform simulator, actively labeling their intent in real-time using steering wheel controls. The dataset comprised approximately 200 lane changes per driver, with environmental parameters such as surrounding vehicle count and speed varied to create diverse scenarios. The model features relative positions, velocities, heading angles, and safety metrics like time-to-collision (TTC) and time headway (THW) derived from a 50-meter detection radius. The authors tested three classification algorithms—Support Vector Machines (SVM), Random Forests (RF), and Logistic Regression (LR)—to determine which best identified the transitions between intent modes based on these environmental features. The results demonstrate that the proposed model can accurately identify driver intent with high precision. SVM achieved the highest overall accuracy at 89.5%, followed by Random Forests at 88.9% and Logistic Regression at 87.2%. Performance remained robust across varying traffic densities, with accuracy ranging from 85.4% to 92.1% depending on the number of surrounding vehicles. Analysis of driver behavior revealed significant variability in the time spent in the "preparing" mode, confirming that intent transitions are driven by environmental dynamics rather than fixed time horizons. The SVM classifier was noted for providing smoother transitions and conservative predictions, effectively balancing noisy human labels. The inclusion of the "preparing" mode allowed the system to predict lane changes before they occurred, with prediction horizons varying based on traffic conditions. The significance of this work lies in its development of a human-inspired driver model that relies on observable environmental cues rather than internal driver states or rigid heuristics. By successfully capturing the decision-making process through a hybrid system, the model offers a flexible tool for autonomous systems to anticipate human behavior in mixed-traffic environments. This capability enhances safety metrics and supports the integration of autonomous vehicles into society by enabling them to convey and interpret intent similarly to human drivers. The authors conclude that this methodology provides a robust foundation for future work, including expansion to more complex roadways and implementation as a high-level controller for autonomous decision-making.
Key finding
Classification algorithms using environmental sensor data achieved high accuracy in identifying driver intent modes, demonstrating that driver decisions are driven by dynamic environmental states rather than fixed time horizons.
Methodology
simulator
Sample size: 5
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 author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 7 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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