  Multi-vehicle collision avoidance is a highly crucial problem due to the
soaring interests of introducing autonomous vehicles into the real world in
recent years. The safety of these vehicles while they complete their objectives
is of paramount importance. Hamilton-Jacobi (HJ) reachability is a promising
tool for guaranteeing safety for low-dimensional systems. However, due to its
exponential complexity in computation time, no reachability-based methods have
been able to guarantee safety for more than three vehicles successfully in
unstructured scenarios. For systems with four or more vehicles,we can only
empirically validate their safety performance.While reachability-based safety
methods enjoy a flexible least-restrictive control strategy, it is challenging
to reason about long-horizon trajectories online because safety at any given
state is determined by looking up its safety value in a pre-computed table that
does not exhibit favorable properties that continuous functions have. This
motivates the problem of improving the safety performance of unstructured
multi-vehicle systems when safety cannot be guaranteed given any
least-restrictive safety-aware collision avoidance algorithm while avoiding
online trajectory optimization. In this paper, we propose a novel approach
using supervised learning to enhance the safety of vehicles by proposing new
initial states in very close neighborhood of the original initial states of
vehicles. Our experiments demonstrate the effectiveness of our proposed
approach and show that vehicles are able to get to their goals with better
safety performance with our approach compared to a baseline approach in
wide-ranging scenarios.
