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Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines

Efficient trajectory planning of autonomous vehicles in complex traffic scenarios is of interest both academically and in automotive industry. Time efficiency and safety are of key importance and here a two-step procedure is proposed. First, a convex optimization problem is solved, formulated as a support vector machine (SVM), in order to represent the surrounding environment of the ego vehicle and classify the search space as obstacles or obstacle free. This gives a reduced complexity search space and an A* algorithm is used in a state space lattice in 4 dimensions including position, heading angle and velocity for simultaneous path and velocity planning. Further, a heuristic derived from the SVM formulation is used in the A* search and a pruning technique is introduced to significantly improve search efficiency. Solutions from the proposed planner is compared to optimal solutions computed using optimal control techniques. Three traffic scenarios, a roundabout scenario and two complex takeover maneuvers, with multiple moving obstacles, are used to illustrate the general applicability of the proposed method.

Mahdi Morsali, Erik Frisk and Jan Åslund

IEEE Transactions on Intelligent Vehicles, Accepted for publication

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