Within the context of trajectory planning for autonomous vehicles this paper proposes methods for efficient encoding of motion primitives in neural networks on top of model-based and gradient-free reinforcement learning. It is distinguished between 5 core aspects: system model, network architecture, training algorithm, training tasks selection and hardware software implementation. For the system model, a kinematic (3-states-2-controls) and a dynamic (16-states-2-controls) vehicle model are compared. For the network architecture, 3 feedforward structures are compared including weighted skip connections. For the training algorithm, virtual velocity constraints and network scheduling are proposed. For the training tasks, different feature vector selections are discussed. For the implementation, aspects of gradient-free learning using 1 GPU and the handling of perturbation noise therefore are discussed. The effects of proposed methods are illustrated in experiments encoding up to 14625 motion primitives. The capabilities of tiny neural networks with as few as 10 scalar parameters when scheduled on vehicle velocity are emphasized.
Regarding control by motion primitives, this approach differs from methods derived from @cite_11 , which require online search of look-up tables, e.g., using a GPU for exhaustive search @cite_5 . In contrast, when encoding motion primitives in a NN, explicit search is not required. Instead, a feature vector relating the current state to a next desired state (waypoint) is fed to the network to generate control commands.
@cite_5 @cite_11
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We present a motion planner for autonomous highway driving that adapts the state lattice framework pioneered for planetary rover navigation to the structured environment of public roadways. The main contribution of this paper is a search space representation that allows the search algorithm to systematically and efficiently explore both spatial and temporal dimensions in real time. This allows the low-level trajectory planner to assume greater responsibility in planning to follow a leading vehicle, perform lane changes, and merge between other vehicles. We show that our algorithm can readily be accelerated on a GPU, and demonstrate it on an autonomous passenger vehicle. We propose a hierarchical control architecture for aggressive maneuvering applicable to autonomous helicopters. In order to reduce the computational requirements of the control problem to be solved to achieve aggressive trajectories, a hybrid system framework is used, which allows for a substantial reduction in the complexity of the system, as well as for guarantees on the stability of the overall behavior. The hybrid controller is based on an automaton whose states represent feasible trajectory primitives. The selection of manoeuvres, and hence the generation of the complete trajectory, can be cast as an optimal control problem that can be solved efficiently in real time.