Prediction of human driver behaviors based on an improved HMM approach
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This paper addresses the challenge of predicting human driver behaviors to enhance Advanced Driver Assistance Systems (ADAS). While traditional systems rely on physical variables like distance and speed, the authors argue that predicting driver intention is critical for safety, as human behavior is the primary cause of accidents. The study proposes an improved Hidden Markov Model (HMM) approach to predict three specific maneuvers: lane keeping, left lane change, and right lane change. The core innovation lies in optimizing a prefilter that quantizes continuous sensor data into discrete observation states, thereby improving the HMM’s ability to infer unobservable hidden states. The methodology models driving maneuvers as hidden states and uses relative velocity and distances to surrounding vehicles as observation variables. Data was collected from nine participants using a professional driving simulator configured for highway scenarios. The HMM parameters were estimated using the Baum-Welch algorithm, and the most probable behavior sequences were calculated via the Viterbi algorithm. To optimize the prefilter thresholds that discretize the observation signals, the authors employed the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The optimization objective minimized a function combining accuracy (ACC), detection rate (DR), and false alarm rate (FAR). The experimental design involved splitting data into training and test subsets to ensure robust model validation. The results demonstrate that optimizing the prefilter significantly enhances prediction performance compared to using general, preset thresholds. For test data, the overall accuracy increased from 68.8% with a general prefilter to 87.5% with the optimal prefilter. Similarly, the detection rate for lane changes improved, while the false alarm rate for right lane changes dropped from 16.9% to 3.5%. When compared against Artificial Neural Networks (ANN), Support Vector Machines (SVM), and combined ANN-SVM models, the optimized HMM achieved the highest detection rate and lowest false alarm rate, as evidenced by Receiver Operating Characteristic (ROC) analysis. Although some specific accuracy metrics for other models were comparable, the HMM’s balance of high detection and low false alarms proved superior. The significance of this work lies in demonstrating that the definition of observation segments (prefiltering) is as critical as the choice of classifier for behavior prediction. By tailoring the prefilter to individual driving data, the system achieves higher reliability in identifying driver intentions. This approach provides a robust framework for ADAS to anticipate maneuvers such as lane changes, potentially allowing for earlier and more accurate safety interventions. The study confirms that HMMs, when combined with optimized signal quantization, are effective tools for modeling complex, stochastic human driving behaviors.
Key finding
Optimizing the prefilter parameters for quantizing observation inputs significantly improves the accuracy and detection rate of Hidden Markov Model-based driver behavior prediction compared to using general preset values.
Methodology
simulator
Sample size: 9
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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
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).
- Theoretical Contribution: computational model