The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models
The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models explores This paper explores the transition to end-to-end learning systems in autonomous driving, highlighting the shift from rule-based approaches to large driving models.. Commercial viability score: 3/10 in Autonomous Driving.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Higher ViabilityWhat Matters for Scalable and Robust Learning in End-to-End Driving Planners?Viability: 7.0
- Higher ViabilityUnleashing the Potential of Diffusion Models for End-to-End Autonomous DrivingViability: 8.0
- Higher ViabilityRisk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous DrivingViability: 6.0
- Higher ViabilitySenna-2: Aligning VLM and End-to-End Driving Policy for Consistent Decision Making and PlanningViability: 7.0
- Higher ViabilityDrive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving