Integrating driving behavior and traffic context through signal symbolization

This paper presents a novel method for integrating driving behavior and traffic context through signal symbolization in order to summarize driving semantics from sensor outputs. The method has been applied to risky lane change detection. Language models (nested Pitman-Yor language model) and speech recognition algorithms (hidden Markov Model) have been utilized for converting continuous sensor signals into a sequence of non-uniform segments (chunks). After symbolization, Latent Dirichlet Allocation (LDA) is used to integrate the symbolized driving behavior and the surrounding vehicle information for establishing the semantics of the driving scene. 988 lane changes of real-world highway driving are used for the evaluation. Risk level of each lane change rated by 10 subjects are used as ground truth. Best results have been obtained when driving behavior and surrounding vehicle information are integrated through co-occurrence chunking after independent symbolization of behavior and context signals.