Before revision: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but  at the cost of efficiency. We present PILOT , a two-stage planning framework comprising an imitation neural network and an efficient optimisation component that guarantees the satisfaction of requirements of safety and comfort .
After revision: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but at the cost of runtime efficiency. On the other hand, naively deploying trajectories produced by efficient-to-run deep imitation learning approaches might risk compromising safety. In this paper, we present PILOT -- a planning framework that comprises an imitation neural network and an efficient optimisation component that guarantees the satisfaction of requirements of safety and comfort .