End-To-End Interpretable Motion Planner for Autonomous Vehicles

This invention describes a smart system for self-driving cars to decide their path. It uses a machine learning model to look at what the car's sensors see and figure out the "cost" or risk of different future locations. Then, it evaluates various possible routes and picks the safest or most efficient one based on these calculated costs.

Why it matters: Since 2023, advancements in large-scale machine learning models and end-to-end learning for robotics have made integrating raw sensor data directly into motion planning more robust. This could simplify the development of the backbone network and cost volume generation.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedHeavy software system, machine learning, sensor integration.

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