Hybrid Trajectory Planning for Autonomous Vehicles using Neural Networks

Highly automated driving, let it be high level advanced driver assistance system (ADAS) or autonomous vehicle is an intensely developing area, in which trajectory planning is an important layer serving multiple needs of safety, feasibility or passenger comfort. To provide dynamically feasible trajectory planning for vehicles based on different control goals one has to deal with the trade-off between numerical resources and correctness. The first part of the paper presents a solution which uses classical optimization based approach that generates the expected results though on the expense of computational effort, which is not acceptable in real-time environment. Two hybrid approaches are introduced in the second part, where the optimization is replaced or aided with artificial neural networks (ANN), trained with the data generated off-line by the first planner. Results show that the solution highly decreases the running time of the algorithm, with almost the same level of performance. The described algorithm can be used as a base for a generic dynamically feasible yet real-time solution.