This work extends our previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules.
In this paper, we present a neural network-based adaptive sampler for motion planning called Deep Sampling-based Motion Planner (DeepSMP).
Many planning applications involve complex relationships defined on high-dimensional, continuous variables.
Predicting the future location of vehicles is essential for safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous driving.
Our aim is to reduce the expected number of collision checks by creating a belief model of the configuration space using results from collision tests.
Bayesian optimization usually assumes that a Bayesian prior is given.