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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Last updated: 2021-11-10
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