arXiv (All)
open access
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving
End-to-end driving policies can achieve strong task performance while offering limited control over their driving style. Driving style describes how a vehicle accelerates, brakes, and maintains distance. Policies trained without explicit preference conditioning lack a direct mechanism for adjusting
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
- Affective Agent: On-Device Personalized Intervention Reasoning for Wearable Systems#1000280
- How do cyclists make their way? - A GPS-based revealed preference study in Copenhagen#428815
- Understanding and Mitigating Corner Cases with Contextual Knowledge in Deep Learning for Automated Driving / Jingxing Zhou ; Jürgen Beyerer, Steven Peters#1052388
- Utah Off-Highway Vehicle Owners' Specialization and Its Relationship to Environmental Attitudes and Motivations#641728
- Business platforms for automated driving systems: a product-service system approach for mobility as a service#1017045
Record · ID 1325698
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