Access to real-world data at scale is the key to solving the toughest challenges in autonomous vehicle development. By harnessing crowdsourced driving data, we can accelerate AV safety, testing, and validation, bringing us closer to mass-market adoption. This session explores how crowdsourced data enhances sensor fusion, improves HD mapping, and provides real-time insights that help AVs navigate complex environments, ensuring safer, more reliable autonomous driving systems.
In this session, you will discover how:
- Crowdsourced data provides the most cost-effective and scalable method for collecting real-world human driving behavior at a massive scale
- Real-world data enhances sensor fusion, improves safety testing, and accelerates faster validation of AV systems in complex, dynamic environments
- Crowdsourced data enables continuous updates to HD maps and real-time insights for improving autonomous vehicle navigation and decision-making
- Data-driven innovation in the safe mass-market adoption of AVs transforms testing, development, and operational efficiency
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