Most imitation learning planners for autonomous driving are trained with displacement based objectives such as Average Displacement Error, which favor average accuracy and overlook how often predicted trajectories become unsafe. CARE Planner augments an attention constrained Transformer with a CVaR based risk module that measures clearance based tail risk along the prediction horizon. This risk is used both to select the supervised trajectory mode and to construct a tail risk aware soft target that downweights unsafe modes during multimodal learning. On the nuPlan benchmark, CARE Planner achieves strong overall performance and improves safety related behavior, indicating that CVaR guided training can make imitation learning planners more reliable.