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• CarPLAN is a novel imitation learning-based motion planning framework that enhances driving context understanding and enables adaptive planning.method 70%
• Displacement-Aware Predictive Encoding (DPE) is introduced to improve the model's spatial awareness by predicting future displacement vectors between the AV and surrounding scene elements.method 70%
• An augmented loss term is incorporated to capture displacement prediction errors, ensuring planning decisions consider relative distances from other agents.method 70%
• Context-Adaptive Multi-Expert Decoder (CMD) is proposed to improve the model's ability to handle diverse driving contexts by dynamically selecting expert decoders based on scene structure.method 70%
• CarPLAN achieves state-of-the-art performance across all closed-loop simulation metrics on the nuPlan benchmark.result 70%
• CarPLAN exhibits robust performance on challenging scenarios such as Test14-Hard on the nuPlan benchmark.result 70%
• CarPLAN demonstrates generalization capability across different benchmark settings, as shown by experiments on the Waymax benchmark.result 70%

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