Real-world data collection at scale:
Billions of miles. All 50 states. True edge case frequency.
Capture reality as it exists, not as you imagine it.
See how we map it city by city with CityStream: bit.ly/43IPN1K#RealWorldData#AVDevelopment
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Our latest deep dive shows how the Foretify development toolchain is used to generate targeted and diverse high-quality synthetic data for training the AI AV stack.
Check it out here: foretellix.com/creating-righโฆ#AutonomousVehicles#SyntheticData#OpenSCENARIO#AVDevelopment
Pony.ai Signs MoU with Luxembourg Mobility Company to Advance Autonomous Vehicle Development
Pony.ai recently announced that on October 23, it signed a Memorandum of Understanding (MoU) with Luxembourgโs leading mobility company, Emile Weber. The two parties will collaborate to advance the development and deployment of autonomous vehicles and technology in Luxembourg.
In September, Pony.ai established its European R&D center in Luxembourg, focusing on cutting-edge autonomous driving research and development, aiming to provide tailored solutions for the European market. This initiative builds on the MoU signed between Pony.ai and the Government of Luxembourg in March, marking further progress in their collaboration.
#PonyAI#AutonomousDriving#Luxembourg#EmileWeber#SelfDrivingCars#TechInnovation#MobilityTech#AVDevelopment
Our latest blog post details how Applied Intuitionโs approach to log extraction ensures reliable ADAS and AD validation across diverse real-world conditions while meeting cost and time constraints.
Learn more: appliedintuition.com/blog/enโฆ#logextraction#AVdevelopment
NVIDIA Dominates CVPR 2024 with Advanced Autonomous Driving Model
NVIDIA Research has secured the top spot in the Autonomous Grand Challenge at the 2024 Computer Vision and Pattern Recognition (CVPR) conference. Their innovative Hydra-MDP model led the leaderboard in the End-to-End Driving at Scale category, outperforming over 400 global entries.
Hydra-MDP, NVIDIA's end-to-end driving system, excels in accurate perception and robust decision-making, offering a unified transformer model. The model's capabilities were demonstrated in CVPR's challenge, where it processed vehicle trajectory history along with camera and lidar data to predict optimal vehicle paths.
NVIDIA's approach simplifies AV development, optimizing pipelines with fewer code requirements and enhanced performance. The Hydra-MDP model can learn from both real-world and simulated data, efficiently handling rare scenarios and mimicking human driving for a smoother experience.
With NVIDIA Omniverse Cloud Sensor RTX APIs, AV developers can test and validate models in virtual environments, marking just the beginning of NVIDIA's breakthroughs in generative physical AI across various industries.
#AIAutonomy#TechInnovation#AVDevelopment#NVIDIAVictory#CVPR2024#HydraMDP#EndToEndDriving#GenerativeAI#AIResearch#NVIDIA