Argo AI is releasing curated data along with high-definition maps to researchers for free, the latest company in the autonomous vehicle industry to open-source some of the information it has captured while developing and testing self-driving cars. The aim, the Ford Motor-backed company says, is to give academic researchers the ability to study the impact that HD maps have on perception and forecasting, such as identifying and tracking objects on the road, and predicting where those objects will move seconds into the future. In short, Argo sees this as a way to encourage more research and hopefully breakthroughs in autonomous vehicle technology. Argo has branded this collection of data and maps Argoverse, which is being released for free. Argo isn't releasing everything it has.
Sophisticated innovations in artificial intelligence, computer vision, tactile sensing and more are the driving forces behind smart and autonomous vehicles. Progress on these fronts will be crucial to making smart cars even more intelligent and bringing self-driving cars to fruition, but industry stakeholders are also concentrating on another key to automotive innovation: open-source data, which can provide more shared tools to propel innovative developments. In one of the most prominent illustrations of this trend, Waymo, the AV subsidiary of Google's parent company, Alphabet, made the Waymo Open Dataset public in 2019. Collected by sensors in Waymo's self-driving cars, the dataset features high-resolution multimodal sensor data that covers a variety of environments, from dense urban centers to suburban landscapes, offering insights into a wide range of driving conditions. Its release came on the heels of Lyft and Argo AI's rollouts of their own open-source datasets, and has since then been followed by the release of the Ford Autonomous Vehicle Dataset and Google's open-sourced Android Automotive OS, among others.
-- Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms. When considering safety, testing algorithms in near-miss scenarios that rarely show up in off-the-shelf datasets is a vital part of the evaluation. As a remedy, we propose a near-miss data synthesizing framework based on V ariational Bayesian methods and term it as Conditional Multiple Trajectory Synthesizer (CMTS). We leverage a generative model conditioned on road maps to bridge safe and collision driving data by representing their distribution in the latent space. By sampling from the near-miss distribution, we can synthesize safety-critical data crucial for understanding traffic scenarios but not shown in neither the original dataset nor the collision dataset. Our experimental results demonstrate that the augmented dataset covers more kinds of driving scenarios, especially the near-miss ones, which help improve the trajectory prediction accuracy and the capability of dealing with risky driving scenarios. Data acquisition vehicles are running on roads and different autonomous driving research institutes have already released their datasets containing millions of data  .
TL;DR, we released the largest and most diverse driving video dataset with rich annotations called BDD100K. You can access the data for research now at http://bdd-data.berkeley.edu. We have recently released an arXiv report on it. And there is still time to participate in our CVPR 2018 challenges! Autonomous driving is poised to change the life in every community.
Robert Bosch Venture Capital GmbH (RBVC), the venture capital arm of global automotive parts supplier Bosch Group, has completed an investment in mapping startup DeepMap Inc, a start-up based in Palo Alto, California that is building high definition maps specifically for self-driving vehicles. DeepMap is focused on solving the mapping and localization challenge for autonomous vehicles. The investment amount was not disclosed. "Maps explicitly designed to be read by machines are a critical enabling technology for safe autonomy. DeepMap fills a vacuum in the market.