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Challenges of Scaling Foundation Models for Autonomous Driving
This chapter explores the intricacies of developing foundation models for autonomous driving by examining the integration of multimodal sensor data, such as LIDAR and cameras. It discusses the trade-offs involved in modeling vehicle trajectories and environmental predictions, emphasizing the need for innovative techniques and a balance between data detail and driving efficiency. The conversation also highlights the importance of world models in simulating driving scenarios to enhance predictive capabilities and driver performance.