Encoding Motion Primitives for Autonomous Vehicles using Virtual Velocity Constraints and Neural Network Scheduling

TL;DR

This paper introduces methods for efficiently encoding motion primitives in neural networks for autonomous vehicles, utilizing virtual velocity constraints and scheduling to improve training and performance.

Contribution

It proposes novel training techniques and network architectures for encoding motion primitives, demonstrating effective encoding with very small neural networks.

Findings

Encoding up to 14625 motion primitives successfully.

Tiny neural networks with as few as 10 parameters are effective.

Virtual velocity constraints enhance training efficiency.

Abstract

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.