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Various sensors, such as LiDAR, cameras and radar collect environmental data surrounding the vehicle. Using sensor fusion technology perception algorithms can determine in real time the type, location, velocity and orientation of objects on the road.
This autonomous perception system is backed by both Baidu’s big data and deep learning technologies, as well as a vast collection of real world labeled driving data. The large-scale deep-learning platform and GPU clusters drastically shorten the learning time for large quantities of data.
Once trained, the new models are deployed onto the vehicle using over-the-air updates through the cloud.
Artificial intelligence and data-driven solutions combine to enable Apollo’s perception system to continuously improve its detection and recognition capabilities, which provide accurate, stable, and reliable input for other autonomous system modules.
Simulation provides the ability to virtually drive millions of kilometers daily using an array of real world traffic and autonomous driving data. Through the simulation service, partners gain access to a large number of autonomous driving scenes to quickly test, validate, and optimize models with comprehensive coverage in a way that is safe and efficient.
Baidu pioneered the extensive application of deep learning and artificial intelligence technology to map creation and is one of the few Chinese firms capable of producing HD mapping data on a large scale.
The localization system is a comprehensive positioning solution with centimeter level accuracy based on GPS, IMU, HD map, and a variety of sensor inputs.
Developers can minimize costs and adjust precision using varied usage scenarios, by customizing the integrated product with selected software and hardware.
Apollo vehicles are equipped with a planning system consisting of prediction, behavior, and motion logic. The planning system adapts to real time traffic conditions, resulting in precise trajectories that are both safe and comfortable. Currently, the planning system operates on a fixed route in both night/day conditions.
The Apollo intelligent vehicle control and canbus-proxy modules are precise, broadly applicable and adaptive to different environments. The modules handle different road conditions, speeds, vehicle types and canbus protocols. Apollo provides waypoint following capability with a control accuracy of ~10 cm.
Baidu has released a variety of datasets for Apollo developers to test their algorithms, train deep NN models. Currently, there are two open datasets, Apollo-SouthBay and Apollo-DaoxiangLake. The data was collected in Silicon Valley, California, the United States, and Daoxiang Lake, Beijing, China. The datasets contain time-stamped LiDAR scans, camera images, and post-processed GPS trajectories.
More than 100 global members join Apollo Partners!