Model Optimization in Deep Learning Based Robot Control for Autonomous Driving

Deep learning (DL) has been successfully used in robotics for perception tasks and end-to-end robot control. In the context of autonomous driving, this work explores and compares a variety of alternatives for model optimization to solve the visual lane-follow application in urban scenarios with an imitation learn- ing approach. The optimization techniques include quantization, pruning, fine-tuning (retraining), and clustering, covering all the options available at the most common DL frameworks. TensorRT optimization for specific cutting-edge hardware devices has been also explored. For the comparison, offline metrics such as mean squared error and inference time are used. In addition, the op- timized models have been evaluated in an online fashion using the autonomous driving state-of-the-art simulator CARLA and an assessment tool called Behavior Metrics, which provides holistic quantitative fine-grain data about robot performance. Typically the performance of robot applications depends both on the quality of the control decisions and also on their frequency. The studied op- timized models significantly increase inference frequency without losing decision quality. The impact of each optimization alone has also been measured. This speed-up allows us to successfully run DL robot-control applications even in limited computing hardware. All the work presented here is open-source, including models, weights, assessment tool, and dataset, for easy replication and extension.