UVA Engineering’s Post

Everyone ready for safer rides? New research boosts self-driving car capabilities 🚗 Great news for the future of transportation! Two new research papers are making significant waves in the field of computer vision for self-driving cars. These papers tackle some of the key challenges facing autonomous vehicles, such as object detection and handling complex road scenarios. According to one of the papers, the use of a 20 frames per second RGB camera combined with an event camera can achieve the same latency as a 5,000-fps camera without compromising accuracy. We’re yet to see what any of this actually looks like, but I am always excited for the prospect of autonomous vehicles and won’t give up on it! Here’s the MSN article with the source: https://lnkd.in/ez-mEPwx #AI #SelfDrivingCars #ComputerVision 

https://lnkd.in/ggJV9FZG End to end perception and prediction for Autonomous vehicles: A Survey #autonomous #ai #tech #latenight #reading #research #needtosleep430amcomesearly

Artificial intelligence (AI) and autonomous vehicles have paved the way for a profound change in transportation. These vehicles are designed to operate without any human intervention as they rely heavily on AI for their functionality. This read delves deeper into the role of AI in autonomous vehicles. https://lnkd.in/gC7aUnRC #annotation #dataannotation #annotationworld #AI #artificialintellegence #machinelearning #imageannotation

A new dataset promises to accelerate the development of autonomous vehicle (AV) technology by providing researchers with a wealth of previously-unavailable real-world driving data captured from multiple vehicles over repeated trips. The MARS (MultiAgent, multitraveRSal, and multimodal) dataset, introduced by researchers from #NYUTandon in collaboration with autonomous vehicle company May Mobility, offers a unique combination of features that sets it apart from previous efforts in the field. The NYU Tandon team presented a paper on MARS last month at the IEEE / CVF Computer Vision and Pattern Recognition (CVPR) Conference, the premier annual computer vision event. The MARS dataset is publicly available. "Datasets for autonomous vehicle research have typically come from a single vehicle’s one-time pass of a certain location. MARS offers many more opportunities because it captures real interactions between multiple AVs traveling over fixed routes hundreds of times, including repeated passes through the same locations under varying conditions," said Chen Feng, the lead researcher on the project. Feng is an NYU Tandon assistant professor working on computer vision for autonomous vehicles and mobile robots, whose National Science Foundation (NSF) CAREER award funded this project. The dataset — curated by Feng’s Automation and Intelligence for Civil Engineering (AI4CE) Lab and May Mobility engineers — was collected using a May Mobility fleet of four autonomous Toyota Sienna Autono-MaaS that operated within a 20-kilometer zone encompassing residential, commercial, and university areas in a U.S. city. Click the link to learn more about this research and to hear from Yiming Li, the first author of this paper & a Ph.D. student in Feng’s lab; Dr. Edwin Olson, CEO & co-founder of May Mobility; and Dr. Fiona Hua, PhD, Director of Autonomy Perception at May Mobility. #NYUTandonMade

Infused with AI, says Ansys's Kishor Ramaswamy, these simulation tools are proving an invaluable tool in the creation of new autonomous vehicle modalities.

Artificial intelligence (AI) and autonomous vehicles have paved the way for a profound change in transportation. These vehicles are designed to operate without any human intervention as they rely heavily on AI for their functionality. This read delves deeper into the role of AI in autonomous vehicles. https://lnkd.in/gC7aUnRC #annotation #dataannotation #annotationworld #AI #artificialintellegence #machinelearning #imageannotation

Here are a few challenges and their solutions when interlinking multi-sensor data in autonomous driving. #Lidar #ADAS #multisensorfusion #data

🚗 Tactile sensor technology is bringing autonomous vehicles closer to human-like decision-making by giving them the ability to "feel" the road! From detecting road conditions to enhancing safety, these sensors are the future of AV innovation. Check out Paul Whytock's article on how this breakthrough could shape the next generation of driverless cars. 👉 Read more: https://lnkd.in/eTKwM3X7 #TactileSensors #AutonomousVehicles #ADAS #SensorFusion #TechnologyInnovation #PaulWhytock