arXiv CS
open access
DriveMLP: When Training Strategy Outperforms Model Complexity in Autonomous Driving
Learning motion planning policies that remain reliable under closed-loop execution is a central challenge in Autonomous Driving (AD). Imitation Learning (IL) scales well to large AD datasets but suffers from distribution shift, causal confusion, and compounding errors under closed-loop execution. In
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- DriveMLP: When Training Strategy Outperforms Model Complexity in Autonomous Driving#1414281
- RIWANav: Recursive World-Action Models with Self-Improvement for Urban Navigation#1218432
- VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning#152982
- EgoSteer: An Open-Source Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos#1202146
- STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation#677380
Record · ID 1406480
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive