Control-Aware Prediction Objectives for Autonomous Driving

Abstract

Autonomous vehicle software is typically structured as a modular pipeline of individual components (e.g., perception, prediction, and planning) to help separate concerns into interpretable sub-tasks. Even when end-to-end training is possible, each module has its own set of objectives used for safety assurance, sample efficiency, regularization, or interpretability. However, intermediate objectives do not always align with overall system performance. For example, optimizing the likelihood of a trajectory prediction module might focus more on easy-to-predict agents than safety-critical or rare behaviors (e.g., jaywalking). In this paper, we present controlaware prediction objectives (CAPOs), to evaluate the downstream effect of predictions on control without requiring the planner be differentiable. We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories. Experimentally, we show our objectives improve overall system performance in suburban driving scenarios using the CARLA simulator.

RAP: Risk-Aware Prediction for Robust Planning

“…The authors trained a road-scene motion forecasting model to produce predictions of other agents that induce diverse reactions from the given robot planner. Similarly, McAllister et al [42] train a model with a weighted loss giving a low weight to the predictions that do not affect the planner. Huang et al [27] train a forecasting model that allows a simple optimization procedure to select the safest among a set of plans generated by a planner.…”