ADAS-TO: A Large-Scale Multimodal Naturalistic Dataset and Empirical Characterization of Human Takeovers during ADAS Engagement
ADAS-TO: A Large-Scale Multimodal Naturalistic Dataset and Empirical Characterization of Human Takeovers during ADAS Engagement explores A dataset of autonomous driving takeovers paired with visual cues enables the development of early warning systems for safer transitions.. Commercial viability score: 7/10 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
- Builds On ThisEngineAD: A Real-World Vehicle Engine Anomaly Detection DatasetViability: 5.0
- Builds On ThisDetection of Autonomous Shuttles in Urban Traffic Images Using Adaptive Residual ContextViability: 4.0
- Prior WorkIntroducing the transitional autonomous vehicle lane-changing dataset: Empirical ExperimentsViability: 7.0
- Prior WorkCRASH: Cognitive Reasoning Agent for Safety Hazards in Autonomous DrivingViability: 7.0
- Prior WorkLongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset