Onboard Monitoring and Reporting for Commercial Motor Vehicle Safety Final Report
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Summary
This report details the development and testing of a prototype Onboard Monitoring System (OBMS) designed to enhance safety for commercial motor vehicles. The research was motivated by the disproportionate impact of large truck crashes, which account for 12% of all traffic fatalities despite representing only 3% of registered vehicles. The project aimed to implement a behavior-based safety approach, where unsafe driving behaviors are identified, monitored, and corrected through feedback, thereby proactively reducing crash risks. The methodology involved a systems engineering process to identify five "core behavioral categories" derived from crash causation literature: speed selection, following behavior, attention, fatigue, and general safety. A hardware and software suite was developed and installed on a Class 8 Freightliner tractor. The system utilized various sensors, including radar, lidar, GPS, accelerometers, steering angle sensors, and eye-tracking cameras, to monitor parameters such as speed, following distance, lane-keeping performance, safety belt use, and turn signal usage. The prototype provided two types of feedback: real-time alerts via visual and auditory cues (e.g., a surrogate instrument cluster displaying recommended speeds and following gaps) and offline reports for carrier management to facilitate training and corrective action. The study found that the prototype successfully measured key indicators of unsafe driving behavior and delivered feedback to drivers. The system was tested on a fleet of 100 drivers in Los Angeles to assess suitability for management and truckers. The report highlights that a small percentage of drivers often account for a large portion of critical incidents, suggesting that objective monitoring can help identify high-risk drivers. The OBMS differed from existing commercial devices by offering a comprehensive ensemble of instruments focused specifically on safety rather than fleet management or location tracking. The significance of this work lies in its demonstration of a viable technology for proactive safety improvement in the commercial trucking industry. By providing immediate and post-trip feedback, the system aims to correct specific deficiencies, such as failure to use turn signals or maintain safe following distances. The report concludes with a plan for Field Operational Testing (FOT) to further evaluate the technical and operational effectiveness of the OBMS suite across a larger fleet, potentially establishing a model for commercial fleets to reduce crashes and injuries through targeted behavioral intervention.
Key finding
A prototype OBMS suite was successfully developed and tested on a Class 8 tractor to monitor and provide feedback on speed, following distance, lane-keeping, safety belt use, and turn signal usage.
Methodology
other
Sample size: 100
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 bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 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.
- truck driver fatigue
- dms validation
- naturalistic crash near crash
- bus coach
- exposure measurement
- 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).
- Empirical Findings: observational prevalence
- Methodological Resource: tool software, validation psychometrics