New sensor models to help autonomous cars to ‘see’ road ahead more clearly

UK-based Claytex, the international consultancy specialising in modelling and simulation, is developing a new generation of sensor models to improve the validation of autonomous vehicle (AV) systems. The new models will help eliminate tragic accidents such as the recent fatality involving an AV and a pedestrian pushing a bicycle in Arizona. One of the contributory factors to that event was failure of the AV’s sensors to correctly identify the obstacle ahead.

“The artificial intelligence (AI) in an AV learns by experience, so must be exposed to many thousands of possible scenarios in order to develop the correct responses. It would be unsafe and impractical to achieve this only through physical testing at a proving ground because of the timescales required,” explains Mike Dempsey, managing director, Claytex. “Instead a process of virtual testing in a simulated environment offers the scope to test many more interactions, more quickly and repeatably, before an AV is used on the public highway.”

rFpro can adjust simulation lighting conditions to match the angle and brightness of the sun

rFpro can adjust simulation lighting conditions to match the angle and brightness of the sun at different

times of day or different latitudes, reflections from a wet road surface and all weather types

 

The challenge has been to ensure that virtual testing is truly representative, and that the AV will respond the same on the road as it did in simulation. Just as a driving simulator must immerse the driver in a convincing virtual reality, the sensor models used to test an AV must accurately reproduce the signals communicated by real sensors in real situations.

“We are initially developing a suite of generic, ideal sensor models for radar, LiDAR and ultrasound sensors, using software from rFpro, with a more extensive library to follow,” says Dempsey. “rFpro has developed solutions for a number of technical limitations that have constrained sensor modelling until now, including new approaches to rendering, beam divergence, sensor motion and camera lens distortion.”

rFpro software renders images using physical modelling techniques, the laws of physics, rather than the computationally efficient special effects developed for the gaming and film industries. This means that rFpro images don’t just convince human viewers, they are also suitable for use with machine vision systems that must be fed sensor data that correlates closely with the real-world.

rFpro Paris Streets Digital Road Model

rFpro Paris Streets Digital Road Model

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