Hazardous Traffic Event Detection Using Markov Blanket and Sequential Minimal Optimization (MB-SMO)
Open accessAbstract
The ability to identify hazardous traffic events is already considered as one of the most effective solutions for reducing the occurrence of crashes. Only certain particular hazardous traffic events have been studied in previous studies, which were mainly based on dedicated video stream data and GPS data. The objective of this study is twofold: (1) the Markov blanket (MB) algorithm is employed to extract the main factors associated with hazardous traffic events; (2) a model is developed to identify hazardous traffic event using driving characteristics, vehicle trajectory, and vehicle position data. Twenty-two licensed drivers were recruited to carry out a natural driving experiment in Wuhan, China, and multi-sensor information data were collected for different types of traffic events. The results indicated that a vehicle’s speed, the standard deviation of speed, the standard deviation of skin conductance, the standard deviation of brake pressure, turn signal, the acceleration of steering, the standard deviation of acceleration, and the acceleration in Z (G) have significant influences on hazardous traffic events. The sequential minimal optimization (SMO) algorithm was adopted to build the identification model, and the accuracy of prediction was higher than 86%. Moreover, compared with other detection algorithms, the MB-SMO algorithm was ranked best in terms of the prediction accuracy. The conclusions can provide reference evidence for the development of dangerous situation warning products and the design of intelligent vehicles.
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For disturbance in data due to various traffic events (hazardous, risky, and safe), the Markov blanket (MB) algorithm was evaluated in a real application of hazardous environmental situations.
“Disturbance in data due to various traffic (hazardous, risky, and safe events) [123] | Markov blanket (MB) algorithm | Evaluation of the proposed approach in real application of hazardous environ-mental situation”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [123]
Methods §2.1 describes using the Markov blanket algorithm for hazardous-traffic-event feature selection, and Results §3.2.2 classifies events as hazardous, risky, or safe. The study used real-life driving data, as reported in Discussions and Conclusions §4. However, Data Preprocessing §3.2.1 attributes abnormal data points to the instability and complexity of the collection system, not to disturbance caused by those traffic events. (LLM, conf=0.84)
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- Authors
- Yan, L., Zhang, Y., He, Y., Gao, S., Zhu, D., Ran, B., & Wu, Q.
- Affiliations
- University of Wisconsin–Madison (US)
- Jinan University (CN)
- Wuhan University of Technology (CN)
- Corresponding author
- He, Yi
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2016-07-13
- Volume
- 16
- Issue
- 7
- Pages
- 1084
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC4970130
- OpenAlex
- W2462308570
- Cited by
- 16
- References
- 39
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 2 · 2019: 3 · 2018: 3 · 2016: 1
- FWCI
- 4.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Nature Science Foundation of China (61273234, 51178364, 61104158)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Engineering › Mechanical Engineering › IoT and GPS-based Vehicle Safety Systems
- Keywords
- Markov blanket, sequential minimal optimization, driver behavior, vehicle trajectories, skin conductance, brake pressure, speed variation, naturalistic driving study, crash prevention, intelligent vehicles
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