The vision of fully autonomous vehicles (AVs) is fast approaching. Along with improving the overall efficiency of transportation systems, driver and passenger safety is the most compelling advantage of self-driving vehicles.
To achieve the next level of vehicle autonomy, the automotive industry faces technological, social, legal, and regulatory concerns. Although many of these issues are difficult to control, technology limitation is one area the automotive community and OEMs can help advance. They can make radar, lidar, cameras, and other sensors smaller, more robust, and less expensive, while improving those sensors’ detection and recognition software. Two obstacles stand in the way of improving the training of those algorithms.
- Closing the gap between roadway and software simulation testing
- Training ADAS / AV algorithms with real-world conditions
This white paper outlines the gaps in autonomous vehicle testing and validation, the key technology advancement to address the challenges, and how you can achieve greater confidence in ADAS functionality with the Keysight Radar Scene Emulator.