I worked (as an intern) on autonomous vehicles at Daimler in 1991. My main project was the vision system, running on a network of transputer nodes programmed in Occam.
The core of the approach was “find prominent horizontal lines, which exhibit symmetry about a vertical axis, and frame-to-frame consistency”.
Finding horizontal lines was done by computing variances in value. Finding symmetry about a vertical axis was relatively easy. Ultimately, a Kalman filter worked best for frame-to-frame tracking. (We processed video in around 120x90 output from variance algorithm, which ran on a PAL video stream.)
There’s probably more computing power on a $10 ESP32 now, but I really enjoyed the experience and challenge.
The core of the approach was “find prominent horizontal lines, which exhibit symmetry about a vertical axis, and frame-to-frame consistency”.
Finding horizontal lines was done by computing variances in value. Finding symmetry about a vertical axis was relatively easy. Ultimately, a Kalman filter worked best for frame-to-frame tracking. (We processed video in around 120x90 output from variance algorithm, which ran on a PAL video stream.)
There’s probably more computing power on a $10 ESP32 now, but I really enjoyed the experience and challenge.
This was our vehicle: https://mercedes-benz-publicarchive.com/marsClassic/en/insta...