CLOVER: Closed-Loop Value Estimation \& Ranking for End-to-End Autonomous Driving Planning
Learn how CLOVER addresses the training-evaluation mismatch in end-to-end autonomous driving planning by introducing a closed-loop value estimation and ranking approach, improving safety and feasibility
- Implement CLOVER using Python and TensorFlow
- Train a value estimation model using logged trajectories
- Evaluate the model using rule-based planning metrics
- Rank alternative trajectories based on their estimated values
- Test the approach in a simulated autonomous driving environment
Autonomous driving researchers and engineers can benefit from CLOVER to improve the performance and reliability of their planning systems, while working together with software engineers to integrate the approach into existing architectures
💡 Closed-loop value estimation and ranking can improve the safety and feasibility of end-to-end autonomous driving planning by addressing the training-evaluation mismatch
🚗💡 CLOVER: Closed-Loop Value Estimation & Ranking for autonomous driving planning!
Key Takeaways
Learn how CLOVER addresses the training-evaluation mismatch in end-to-end autonomous driving planning by introducing a closed-loop value estimation and ranking approach, improving safety and feasibility
DeepCamp AI