Robust Lane-Change Recognition Based on An Adaptive Hidden Markov Model Using Measurement Uncertainty
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Lane-changing of surrounding vehicles is a risky situation because vehicle accidents can be easily caused by driver’s unawareness of the surrounding vehicle. Much research has conducted on lane-change recognition (LCR) to avoid these vehicle accidents by warning drivers. LCR is a technology to estimate lane-changing behaviors of surrounding vehicles from observation data: position, velocity, and lane information. Since these observation data change continuously during lanechanging, most research for LCR has used time series data based on hidden Markov model (HMM). A challenging point of LCR is that HMM could make false positives in LCR when the observation data include uncertainties such as sensor noise and object detection error. Previous research has tried to process observation data by using Bayesian filter. However, the approach cannot remove all data uncertainties. This paper proposes a method for using observation uncertainty through an adaptive HMM for LCR. In the method, HMM models are modified in real time based on data covariance to filter data with high uncertainty. For evaluation of the algorithm, it was tested through 71 lane-changing cases in real driving situations. The results show that the proposed method enhanced the recognition accuracy by 25.3 % (63.3 % → 88.7 %) than a previous LCR method.
Key WordsLane-change recognition Hidden Markov model Automotive applications Observation uncertainty
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