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A Novel Framework for Road Traffic Risk Assessment with HMM-Based Prediction Model
Xunjia Zheng1, Di Zhang2, Hongbo Gao3
1State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China. zhengxj15@mails.tsinghua.edu.cn.
Sensors (Basel, Switzerland)
|December 15, 2018
Summary
This study introduces a novel road traffic risk assessment method for intelligent vehicles using Hidden Markov Models (HMM). The approach predicts steering angle status to enhance road safety and prevent accidents.
Area of Science:
- Intelligent Transportation Systems
- Vehicle Dynamics and Control
- Road Safety Engineering
Background:
- Intelligent vehicles and V2X systems have seen extensive research over decades.
- Accurate road traffic risk assessment is crucial for accident prevention in intelligent vehicles.
Purpose of the Study:
- To propose a novel road traffic risk assessment method for intelligent vehicles.
- To enhance the safety and accident prevention capabilities of intelligent vehicles.
Main Methods:
- Utilizing Hidden Markov Models (HMM) for risk assessment.
- Predicting steering angle status to evaluate probabilities in independent intervals.
- Calculating road traffic risk across different analysis regions.
Main Results:
- Quantified road traffic risk presented visually via time-varying risk maps.
- Demonstrated effectiveness of the assessment and prediction strategies through experimental results.
Conclusions:
- The proposed HMM-based method provides accurate road traffic risk assessment.
- The time-varying risk map visualization aids in understanding and predicting traffic risks for intelligent vehicles.
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