CLAP-AD TFv6
TFv6 checkpoint for closed-loop evaluation on Bench2Drive-Speed.
82.53
Speed-Adherence Score
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AI & ML interests
User-Customizability in End-to-End Autonomous Driving
Recent Activity
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Organization Card
Research in end-to-end driving
Planning for the closed loop.
CLAP-AD explores imitation-free speed scoring to make end-to-end driving policies more aware of the outcomes their actions create.
Model releases
Checkpoints
Open weights, configs, and benchmark details.
CLAP-AD LTFv6
LTFv6 checkpoint adapted to NAVSIM's four-second planning horizon.
38.0
Overall EPDMS · two-stage protocol
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Each model repository includes its checkpoint, configuration, evaluation details, intended-use notes, and license information. Scores follow the protocols documented in the model cards.
About the work
From imitation to outcome-aware planning.
CLAP-AD studies how speed selection and planning can account for closed-loop driving outcomes. The method and experiments span both CARLA and NAVSIM.
Intended use
Research artifacts
These checkpoints are for benchmark evaluation. They have not been validated for real-vehicle deployment or other safety-critical use.
Citation
Build on the research
@inproceedings{aghajanzadeh2027clapad,
title={Closed-Loop Aware Planning via Imitation-Free Speed Scoring for End-to-End Autonomous Driving},
author={Aghajanzadeh, Emad and Schwarzfischer, Thomas and Kaufeld, Marc and Thoma, Moritz and P{\'e}goud, Ryan and Vemparala, Manoj Rohit and Mori, Pierpaolo and Fasfous, Nael and Betz, Johannes and Mueller-Gritschneder, Daniel and Schlichtmann, Ulf},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
year={2027}
} models 0
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datasets 0
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