Overview

Autonomous cars contain the most complex hardware and software ever deployed by automakers. The software must process a flood of data from sensors such as cameras, LiDAR and radar in real time to form a model of the car’s surroundings and make safe decisions on control of the vehicle. This requires highly efficient, safe and secure software that can make use of special purpose hardware (accelerators) for vision processing and deep neural-net based machine learning algorithms. The QNX Platform for ADAS provides a functionally safe software foundation that enables the above functions to deliver an advanced driver assistance system (ADAS) for automated driving.

The QNX Platform for ADAS is built upon the QNX OS for Safety, which is certified by TÜV Rheinland to ISO 26262 ASIL D. This certification also includes tool chain qualification for the C compiler, assembler and linker at level TCL 3.

  • Flexible platform approach for multiple ADAS and automated driving applications
  • Engineered for distributed processing
  • Optimized for automotive silicon

Product in Action

In the News

The QNX Platform for ADAS was recognized at the 2017 TU-Automotive Awards as the Best Active Safety or ADAS Product/Service.

Benefits

  • Flexible platform approach for multiple ADAS and automated driving applications
  • Engineered for distributed processing
  • Optimized for automotive silicon

Technology

The QNX Platform for ADAS offers numerous features, including:

  • Reference implementations for four camera surround view, single camera ADAS, sensor hub with multi-camera input
  • Low latency sensor data acquisition: support for camera, radar, LiDAR, IMU, GPS sensors
  • Publish and subscribe sensor data access
  • Data visualization
  • Network plugins to provide sensor data over automotive networks
  • Sensor data capture with time stamped data samples. Sensor data playback maintains timing fidelity
  • Configurable timestamp sources such as IEEE 1588 PTP or IEEE 802.1AS
  • Robot OS (ROS) integration for testing and prototyping. Export data using ROS to compatible tools such as Matlabs
  • Integrated open source libraries including OpenCV, SOME/IP, Ceres and others