Passing‐yielding intention estimation during lane change conflict: A semantic‐based Bayesian inference method

Cui, Man; Liu, Jinxin; Zheng, Haotian; Xu, Qing; Wang, Jiangqiang; Li, Geng; Sekiguchi, Tomoko

doi:10.1049/itr2.12410

Cited by 1 publication

(1 citation statement)

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“…Thus, discriminative models failing to grasp the temporal nuances within data might misinterpret a driver's genuine intentions. In contrast, sequential models, such as RNN [15][16][17], dynamic Bayesian networks (DBN) [18,19], and HMM, are specifically designed to capture and process temporal dependencies in data. For tasks like driving intention recognition, these sequential models may offer more accurate and reliable results [20].…”

Section: Related Workmentioning

confidence: 99%

Driving Intention Recognition of Surrounding Vehicles Based on a Time-Sequenced Weights Hidden Markov Model for Autonomous Driving

Liu,

Qu,

Gao

et al. 2023

Sensors

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Accurate perception, especially situational awareness, is central to the evolution of autonomous driving. This necessitates understanding both the traffic conditions and driving intentions of surrounding vehicles. Given the unobservable nature of driving intentions, the hidden Markov model (HMM) has emerged as a popular tool for intention recognition, owing to its ability to relate observable and hidden variables. However, HMM does not account for the inconsistencies present in time series data, which are crucial for intention recognition. Specifically, HMM overlooks the fact that recent observations offer more reliable insights into a vehicle’s driving intention. To address the aforementioned limitations, we introduce a time-sequenced weights hidden Markov model (TSWHMM). This model amplifies the significance of recent observations in recognition by integrating a discount factor during the observation sequence probability computation, making it more aligned with practical requirements. Regarding the model’s input, in addition to easily accessible states of a target vehicle, such as lateral speed and heading angle, we also introduced lane hazard factors that reflect collision risks to capture the traffic environment information surrounding the vehicle. Experiments on the HighD dataset show that TSWHMM achieves recognition accuracies of 94.9% and 93.4% for left and right lane changes, surpassing both HMM and recurrent neural networks (RNN). Moreover, TSWHMM recognizes lane-changing intentions earlier than its counterparts. In tests involving more complex roundabout scenarios, TSWHMM achieves an accuracy of 87.3% and can recognize vehicles’ intentions to exit the roundabout 2.09 s in advance.

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