To guide an automated vehicle safely through complex traffic, knowledge about the future evolution of the driving situation has to be considered. The contribution at hand proposes an approach for automated driving in structured environments. An environment representation for trajectory planning is presented that enables predictive driving by interconnecting trajectory prediction for the surrounding traffic and planning of an according ego trajectory. A maneuver-based approach with an efficient trajectory model is carried out to enable an accurate and fast estimation of future motions of other vehicles. The results are directly considered in the dynamic environment representation utilized for trajectory planning. In the trajectory optimization process, the required safety distance is hence taken into account uniquely for each future time instance. The evaluation of the trajectory prediction approach shows very good performance on a simulated as well as on a dataset recorded by a test vehicle. On account of the predictive character of the developed environment potential field, simulation experiments demonstrate the feasibility and effectiveness of the proposed method.
Predictive Driving: Fusing Prediction and Planning for Automated Highway Driving
IEEE Transactions on Intelligent Vehicles ; 4 , 3 ; 456-467
2019-09-01
3071237 byte
Article (Journal)
Electronic Resource
English
ADAPTIVE VEHICLE LONGITUDINAL TRAJECTORY PREDICTION FOR AUTOMATED HIGHWAY DRIVING
British Library Conference Proceedings | 2016
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