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Intention-aware Lane Changing Assistance Strategy Basing on Traffic Situation Assessment
Technical Paper
2020-01-0127
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Traffic accidents avoidance is one of the main advantages for automated vehicles. As one of the main causes of vehicle collision accidents, lane changing of the ego vehicle in case that the obstacle vehicles appear in the blind spot with uncertain motion intentions is one of the main goals for the automated vehicle. An intention-aware lane changing collision assistance strategy basing on traffic situation assessment in the complex traffic scenarios is proposed in this paper. Typical Regions of Interest (ROI) within the detection range of the blind spots are selected basing on the road topology structures and state space consisting of the ego vehicle and the obstacle vehicles. Then the motion intentions of the obstacle vehicles in ROI are identified basing on Gaussian Mixture Models (GMM) and the corresponding motion trajectories are predicted basing on the state equation. Traffic situation is assessed according to the index of the motion intentions and the coupling tendency between the ego vehicle and the obstacle vehicles and the risk level is graded basing on the map with collision time. Lane keeping assist is carried out according to the assessment result of the traffic situation. Testing scenarios with the straight road and T-junction are designed and a co-simulation environment consisting of CarMaker and Mathwork Simulink is established to verify the proposed strategy in complex traffic scenes. Simulation results present an adaptive ROI and a high identification accuracy for motion intentions of the obstacle vehicles. What’s more, it shows that the traffic situation can be accurately evaluated and the ego vehicle can be effectively controlled with the appearance of the high-risk vehicles.
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Wu, J., Liu, S., He, R., and Sun, B., "Intention-aware Lane Changing Assistance Strategy Basing on Traffic Situation Assessment," SAE Technical Paper 2020-01-0127, 2020, https://doi.org/10.4271/2020-01-0127.Data Sets - Support Documents
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