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.
End-to-End autonomous solutions are attractive because of the low cost and low engineering complexity. By using real road data, the horizontal and latitude driving models are based entirely on deep learning. This allows for the quick and efficient application onto autonomous test vehicles. Currently, horizontal, and latitude model source code with 10,000 km of data is available on Apollo.
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.
By opening the autonomous driving source code, capabilities, and data, Apollo forms a comprehensive "vehicle and cloud" open ecosystem. Apollo offers developers and partners lacking data and computing power an array of fast and flexible services. Through this, Apollo is building an open autonomous driving ecosystem that empowers each participant and broadens the widespread adoption of autonomous driving.
Apollo provides partners with a complete hardware reference design, including vehicle selection, key hardware components, peripherals, and a multifaceted hardware installation guide. This guide details the hardware installation process and offers a starting point for integration with software and outlines vehicle road testing.
The MAP Engine manages and protects the HD-Map data, as well as provides a unified data query interface. The MAP Engine includes core capabilities such as:
• Element retrieval
• Spatial retrieval
• Format adaptation
• Cache management
The MAP Engine provides a modular, hierarchical, cross platform, and a highly customizable programming interface that allows users to easily build map solutions.
A mass-produced and complete operating system solution to internet of vehicles (IOV).
Apollo offers the 4S solution – Scan, Shield, See, and Save. This covers the full life-cycle of a vehicle's cyber security needs. For Shield, Apollo’s security products are currently deployed in mass production vehicles, including IDPS, Car FireWall, Secured OTA Kits, in order to protect user privacy and vehicle information from network security breaches.