Joint learning of video images and physiological signals for lane-changing behavior prediction

Gao, Jun; Yi, Jiangang; Murphey, Yi Lu

doi:10.1080/23249935.2021.1936279

Cited by 6 publications

(2 citation statements)

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“…The experimental results effectively improved the safety of a lane change. In Equations ( 9) to (12), r represents the reset gate, z represents the update gate, and h represents the contents of the current memory. s k t represents the memory of the adjacent vehicles k with the current time step, σ represents the sigmoid activation function, x k t represents the coordinates x k ij , y k ij of the adjacent vehicles k at time t, U i represents the update transfer matrix, W i represents the weight matrix, i = r, z, h, and the symbol ⊗ represents the multiplication of vector elements.…”

Section: Mixed Models Of Lane Changementioning

confidence: 99%

“…Lane change models are usually composed of vehicle dynamic parameters, vehicle driving states, and driver characteristic indicators. The vehicle dynamic parameters include the steering wheel angle, lateral speed, steering speed, and brake pedal position [7][8][9], and the driver's characteristic indicators include head movement, eye movement, limb movement, and EEG changes [10][11][12][13]. The driving state of the vehicle includes speed, acceleration, driving direction, and other factors [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Lane Change Models—A Historical Review

Liu,

Hong,

Lin

2023

Applied Sciences

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Lane changing is a complex operation that has a significant impact on traffic safety. The accurate identification and assessment of potential risks in the driving environment before lane changing is crucial for the safe and smooth completion of a lane change. In this paper, the research status of vehicle lane change models is reviewed. Firstly, various factors affecting lane change models are analyzed. Different drivers will be affected by vehicle dynamic parameters, vehicle driving states, and driver characteristics under various road environments. Secondly, the vehicle lane change models are divided into four types: the empirical model of lane changing, the physical model of lane changing, the cognitive model of lane changing, and the mixed model of lane changing. The advantages and disadvantages of different types of lane change models are analyzed, and the key problems to be solved by different lane change models are expounded, respectively, from the aspects of input variables and reasoning algorithms. Finally, according to the advantages and disadvantages of different lane change models, a future research direction is proposed.

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Section: Mixed Models Of Lane Changementioning

confidence: 99%