Harden the Robustness of Your Autonomous Driving Algorithms

Article By : Janet Ooi, Keysight Technologies

Developing autonomous driving algorithms is complex because the task involves many variables, including replicating complex, repeatable scenes in a lab environment.

Challenges in Developing Autonomous Vehicles

As you develop autonomous vehicles (AVs), how do you ensure your automotive radar sensors “see” the intended scenarios through software simulations early in the development cycle? How do you know that your lab tests are thorough enough to ensure vehicle safety on the road?

Developing autonomous driving algorithms is complex because the task involves many variables, including replicating complex, repeatable scenes in a lab environment. The more accurate the scenes, the faster your algorithms can be trained. This is where current in-lab solutions fall short.

The Evolution of Radar Sensors

Radar sensors have evolved significantly over the last decade. In the automotive industry, radar sensors are a critical part of Advanced Driver Assistance Systems (ADAS) and enable features such as blind-spot detection, lane departure warning or correction, automatic emergency braking, and so much more.

Radar technology continues to evolve with higher frequencies, wider bandwidth, and better resolution. In fact, advances in radar sensor technology push the automation level in vehicles to Level 3+ or 4, requiring the test and validation of more and more scenarios. As a result, automotive original equipment manufacturers (OEMs) and Tier 1 suppliers need to perform more tests with a higher degree of complexity.

Reimagine Test Tactics

Picture a setting in an urban area with high population density, many road intersections, and turning scenarios. There are numerous pedestrians, cyclists, e-scooters, and even the three-wheeled cargo delivery bike.

The conventional way of testing the functionalities and algorithms of the radar sensors is by driving on roads for millions of miles. But this tactic will not be able to cover all the potential scenarios, including the one-in-a-million scenarios. In fact, the majority of the tests that you need to develop and validate AV systems need to go through simulation much earlier in the development cycle.


In this month’s In Focus, we look at the developments in the automotive electronics sector, supply/demand scenario, and how suppliers are keeping up with rising demand.

 


But how?

Automotive OEMs need to emulate real-world scenarios that enable validation of actual sensors, electronic control unit code, artificial intelligence logic, and more. Testing the physical hardware in a simulated environment close to real-world scenarios ensures that autonomous vehicles will behave as expected on the road.

The simulated environment and the rendered conditions need to include vehicle dynamics, weather conditions, and surrounding objects as well as real-time responses, in order to test the responses of the radar sensors. However, gaps remain in the technology today that hinder real-world scene renderings.

Technology Gaps in Radar Target Simulation 

  • Number of targets and field of view

Some systems use multiple radar target simulators (RTSs). Each presenting point targets to radar sensors and simulates, horizontal and vertical positions by mechanically moving antennas around. Mechanical automation slows overall test time. Other solutions create a wall of antennas with only a few RTSs. These solutions only allow the radar sensor to see a handful of objects within a very narrow angle in front of it where blind spots can occur. 

  • Minimum distance

Realistic traffic scenes require objects to be emulated very close to the radar unit.  For example, approaching a stoplight where cars are 2 meters or less apart, bikes or scooters might move in the lane, and pedestrians might cross the road very near to the car. Passing this test is of utmost importance to your ADAS autonomous vehicle’s safety features.

  • Resolution between objects

The resolution between objects refers to the details of the scene and the confidence to know that the algorithm you are testing can distinguish between two objects that are close together.  If you cannot identify the objects correctly, it is difficult to fully test the sensors, the algorithms, and the decisions that rely on the data streaming from the radar sensors.

Enable Next-Generation Vehicle Autonomy with In-Lab Full Scene Emulation

The robustness of autonomous driving (AD) algorithms depends on how comprehensive the testing is. This is why Keysight created the Radar Scene Emulator (RSE). Keysight RSE enables OEMs in the automotive industry to test autonomous drive systems with radar sensors faster and with highly complex, multi-target scenes. The RSE allows you to create scenarios with up to 512 objects, and at distances as close as 1.5 meters from the vehicle.

The scenarios can also have dependent attributes, including speed, direction, distance from the vehicle, angle, and more.

With Keysight’s Radar Scene Emulator, automotive OEMs can shift testing of complex driving scenarios to the lab. This eliminates the need to drive millions of miles and dramatically accelerates the speed of testing. Using RSE’s industry-first approach, By thoroughly testing decisions earlier in the cycle against complex, repeatable, high-density scenes, and with stationary objects or objects in motion automotive OEMs can accelerate the insights that come from ADAS or AD algorithms.

Sharpen Your ADAS Radar Vision – learn more about Keysight Radar Scene Emulator.

 

Subscribe to Newsletter

Leave a comment