|Effect of delays/lag and fighting it in self-driving neural networks|
|Organisatsiooni nimi||Autonomous Driving Lab|
|Kokkuvõte||For camera-based self-driving neural network models, we collect a dataset of image and driving command pairs. This dataset connects a camera image with how the human driver steered the car at that moment. We then train a network that would take as input the camera feed and would output the driving command (predicting what would a human do here?).|
However, the networks take time to compute their answers and the command reaches the wheels with a delay. Also, the motors turning the wheels have intrinsic delays. This means that even if the model can perfectly predict what a human would do in the situation, the answer is found too late and the car will not stay on track.
In this project:
-You evaluate the amount of delay, T milliseconds, the network computations infer
-You train models that predict not the steering command captured at the same time as the image, but T milliseconds after that. So the network would incorporate our knowledge about its delays.
-Compare prediction accuracy - is predicting future command even possible based on the present image?
-Compare driving ability - will the cars stay on the road better?
This project will be mainly done with toy cars. Once we prove the concept that predicting future commands is useful, a model can be trained also on real data for the Lexus. Performance on the Lexus will be evaluated qualitatively (does it feel safer, smoother?).
This may be publishable work.
|Lõputöö kaitsmise aasta||2021-2022|
|Suhtlemiskeel(ed)||eesti keel, inglise keel|
|Märksõnad||#self-driving, AI, neural networks, deep learning|