Discretionary Lane Change Model for Intelligent Connected Vehicles on Expressway

Zhou, Shu-Tong; Zhang, Jian; Li, Linheng; Wu, Kunrun

doi:10.1061/9780784482292.488

Cited by 2 publications

(2 citation statements)

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“…[18] presented a lane‐change model on the highway based on the evaluation of desirability, which is related to average travel time, average time gap, and remaining travel time. In addition to the ego vehicle's speed and acceleration, the distance [19, 20] and differences of speed [21] and acceleration [22] between the car and its surrounding vehicles are also very important for the lane‐change motivation. Jin et al.…”

Section: Introductionmentioning

confidence: 99%

“…Different from [15,17], Nilsson et al [18] presented a lane-change model on the highway based on the evaluation of desirability, which is related to average travel time, average time gap, and remaining travel time. In addition to the ego vehicle's speed and acceleration, the distance [19,20] and differences of speed [21] and acceleration [22] between the car and its surrounding vehicles are also very important for the lanechange motivation. Jin et al [19] proposed the motivation as to have a further position within 9 s. Both the gap distance between the ego vehicle and its preceding vehicle and velocity of the two vehicles were considered.…”

Section: Introductionmentioning

confidence: 99%

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Event‐triggered optimisation of overtaking decision‐making strategy for autonomous driving on highway

Huang

Zhang

Chen

et al. 2022

IET Intelligent Trans Sys

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Appropriate overtaking can improve the safety and efficiency of driving. Practically, overtaking consists of two asymmetric lane-changes: firstly to the left lane and then return back. Existing literatures treated the two lane-changes as the same behaviour, which may result in frequent and unnecessary lane-changes. For the more complex first lane-change, discretionary lane-change strategies have been developed to gain speed advantage with least deceleration. Most of them, however, described vehicles' dynamics with rough simplifications different from actual execution (e.g. constant speed and constant acceleration), which may fail them in practical applications. This paper presents an event-triggered overtaking decision-making strategy for autonomous vehicles to make the overtaking meet the driver's expectations. Firstly, the difference between the left-lane change and right-lane change is analysed. Left-lane change is mainly concerned with the efficiency, whereas driving safety is the main target of the right-lane change. Secondly, an event-triggering condition covering the speed difference between the ego vehicle and its preceding vehicle is set. Afterwards, based on quantified speed advantage, considering surrounding vehicles' changes in both speed and acceleration, an optimisation of overtaking decision-making strategy is proposed. During right-lane change in an overtaking, safety is ensured by predicting whether the ego vehicle will collide with surrounding vehicles. Finally, several scenarios are defined in the combination of SCANeR studio and Simulink, which are used to test and demonstrate the effectiveness and applicability of the overtaking decision-making strategy. The results show that the proposed overtaking decision-making strategy, by mathematical quantification of speed advantage, performs well in measuring the motivation of lane-change and avoiding frequent lane changes, which correspond to driver habits.

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