By adapting vehicle control systems to the skill level of the driver, the overall vehicle active safety provided to the driver can be further enhanced for the existing active vehicle controls, such as ABS, Traction Control, Vehicle Stability Enhancement Systems. As a follow-up to the feasibility study in [1], this paper provides some recent results on data-driven driving skill characterization. In particular, the paper presents an enhancement of discriminant features, the comparison of three different learning algorithms for recognizer design, and the performance enhancement with decision fusion. The paper concludes with the discussions of the experimental results and some of the future work.
Data-Driven Driving Skill Characterization: Algorithm Comparison and Decision Fusion
Sae Technical Papers
SAE World Congress & Exhibition ; 2009
2009-04-20
Conference paper
English
Data-driven driving skill characterization: algorithm comparison and decision fusion
Automotive engineering | 2009
|2009-01-1286 Data-Driven Driving Skill Characterization: Algorithm Comparison and Decision Fusion
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