Abstract

Imitation-learning planners for autonomous driving commonly optimize displacement-based objectives, which improve average trajectory accuracy but may overlook rare unsafe modes. This paper presents CARE Planner, a risk-aware extension of CAR Planner that combines constrained ego-state attention with a Conditional Value at Risk (CVaR)-based tail-risk module. The proposed module estimates clearance-based risk along the prediction horizon, adjusts supervised mode selection toward safer candidates, and constructs risk-aware soft targets for multimodal trajectory learning. The attention constraint prevents excessive dependence on a small subset of ego-state channels, while the CVaR-based module reshapes the output distribution away from high-risk modes. Experiments on the nuPlan test14-random and test14-hard splits show improved open-loop and closedloop performance, and a pedestrian-waiting scenario analysis shows a reduced high-risk trajectory distribution.