Abstract：An accurate lane change intention recognition algorithm is developed to improve real-time performance. The algorithm analyzes the drivers' lane change decisions to develop a new symbol and a comprehensive decision index (CDI) based on fuzzy theory to assess the probability that the driver will change lanes. Then, the driver intention recognition algorithm is designed based on a hidden Markov model. Using the new symbol as well as representative lateral motion parameters as observed signals, and the driver's intention as the hidden state, a hidden Markov model is built and trained. The driver's intention is recognized by the HMM decoding method. Lane change data collected on a driving simulator are used to verify the overall algorithm performance. The results show that the algorithm with the CDI as one of the observation signals both guarantees the accuracy of the recognition results and improves the real-time performance.
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