2 research outputs found
Encoding Motion Primitives for Autonomous Vehicles using Virtual Velocity Constraints and Neural Network Scheduling
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.Comment: 8 pages, 4 figures, 7 tables, ICMLA 201
Online Sampling in the Parameter Space of a Neural Network for GPU-accelerated Motion Planning of Autonomous Vehicles
This paper proposes online sampling in the parameter space of a neural
network for GPU-accelerated motion planning of autonomous vehicles. Neural
networks are used as controller parametrization since they can handle nonlinear
non-convex systems and their complexity does not scale with prediction horizon
length. Network parametrizations are sampled at each sampling time and then
held constant throughout the prediction horizon. Controls still vary over the
prediction horizon due to varying feature vectors fed to the network.
Full-dimensional vehicles are modeled by polytopes. Under the assumption of
obstacle point data, and their extrapolation over a prediction horizon under
constant velocity assumption, collision avoidance reduces to linear inequality
checks. Steering and longitudinal acceleration controls are determined
simultaneously. The proposed method is designed for parallelization and
therefore well-suited to benefit from continuing advancements in hardware such
as GPUs. Characteristics of proposed method are illustrated in 5 numerical
simulation experiments including dynamic obstacle avoidance, waypoint tracking
requiring alternating forward and reverse driving with maximal steering, and a
reverse parking scenario.Comment: 8 pages, 8 figures, 3 tables, conference pape