Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicles safely, we present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. The objective of the presented dataset is to advance research in understanding the structure of road scenes by examining the relationship between perceived entities, such as traffic elements and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. It comprises three primary sub-tasks, including the 3D lane detection inherited from OpenLane, accompanied by corresponding metrics to evaluate the model's performance. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicles safely, we present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. The objective of the presented dataset is to advance research in understanding the structure of road scenes by examining the relationship between perceived entities, such as traffic elements and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes.

Momenta is now the recipient of a $500 million investment round led by Toyota in order to provide new technologies like automated HD mapping and updates through vision-based technologies, a company announcement says. Both companies are looking to promote their new Toyota Automated Mapping platform for Chinese customers. Momenta specializes in vision-based, or camera-based, HD mapping, which utilizes camera, GPS and IMU to automatically generate HD maps. The maps are of a high quality through cutting-edge technologies like "deep-learning based perception, SLAM (simultaneous localization and mapping), etc.," and come with rich geometry features like traffic signs, poles, lane borders, traffic lights and road markings. In addition, the tech generates road-level and lane topology and semantic features.

The world now knows that Tesla's Elon Musk thinks that high-precision GPS maps for self-driving cars are a "really bad idea." During the company's Autonomy Day in April, Musk made it abundantly clear that too much dependency on HD Maps can turn an autonomous vehicle (AV) into a "system that becomes extremely brittle," making it more difficult to adapt. The rest of the automotive industry, however, pretty much believes that AV could use an HD map as, at least, a backup system. "HD Maps are all about adding intelligence to improve the performance and safety of automated vehicles," said Phil Magney, founder and principal at VSI Labs. As Matt Preyss, Product Marketing Manager at HERE, explained, HD maps are not the familiar GPS helpers used by human drivers.

Civil Maps, creator of the world's first edge-based HD mapping and localization platform for self-driving cars, today announced it has teamed up with Renovo, the software technology company behind AWare, to provide highly automated vehicle makers and technology providers with seamless access to Civil Maps' vehicular cognition stack. Through this technical collaboration, self-driving systems and other automotive modules that integrate with Renovo's AWare, the first OS built specifically for automated mobility, will be immediately compatible with several key aspects of Civil Maps' platform, a lightweight, highly scalable solution to HD map creation, usage, and continental-scale crowdsourcing. Moving forward, the two companies will work together to standardize abstraction layers that sit between Civil Maps' mapping and localization systems and OEM sensor configurations, decision engines, human machine interfaces (HMIs), and control systems. This collaboration will result in a universal interface, architected by Renovo, that will provide plug-and-play compatibility with Civil Maps' vehicular cognition stack for all other modules in the fast-growing AWare ecosystem, thereby providing significant time and cost savings for developers. "Interoperability is the right direction for the industry and we are excited to take this step forward with Renovo," said Sravan Puttagunta, CEO and Co-founder of Civil Maps.

It may have been a while since we last we came across a Here 3D mapping vehicle, but that's not to say the company hasn't been using its cars lately. In fact, the Here True collection vehicle is now in its third revision, and I got to hop on one -- based on a Volkswagen Golf Variant 280 TSI Highline -- during Computex. The ride features much faster D-GPS tracking that no longer requires a half-hour calibration (to reach an accuracy of under one meter), along with a Velodyne LiDAR with an accuracy of better than 2cm (within a range of up to 70 meters) and four 16.2-megapixel MARS panoramic cameras. This set of gear is almost identical to what we've seen before, so the real highlight this time is the updated backend to support high-definition mapping. For HD mapping, Here's fleet is currently involved in over 20 projects worldwide and covering over 48,200 km per week, with each collection vehicle contributing around 500 km or over 1TB worth of data.