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

Abstract

Abstract 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.