Key Factors that Negatively Affect Performance of Imitation Learning for Autonomous Driving

Abstract

Conditional imitation learning (CIL) has proven superior to other autonomous driving (AD) algorithms. However, its performance evaluation through physical implementations is still limited. This work contributes a systematic evaluation to identify key factors potentially improving its performance. It modified convolutional neural network parameter values, such as reducing the number of filter channels and neuron units, and implemented the model into a vision-based autonomous vehicle (AV). The AV has front-wheel steering with an Ackermann mechanism since it is commonly used by passenger cars. Using the Inertia Measurement Unit, we measured the vehicle’s location and yaw angle along the experimental route. The AV had to move autonomously through new road sectors in the morning, afternoon, and night. First, an overall performance evaluation was carried out. The results showed a 99% success rate from 648 evaluation experiments under different conditions in which the 1% failure rate happened at new intersections. Then, a turning performance evaluation was conducted to identify key factors leading to failure at new intersections. They include fast speed, dazzling light reflection, late navigation command change instant, and the untrained turning driving pattern. The AV never failed while driving on the trained routes. It had a 100% success rate when driving slower, even under various lighting conditions and at various driving patterns, including untrained intersections. Although this study is limited to identifying key factors at three constant speeds, the results become the foundation for future research to improve CIL performance for AD, including by incorporating multimodal fusion and multi-route networks.