Indicators of Learning Ability of Autonomous Vehicles

With the increase of research on autonomous vehicles, the evaluation research of autonomous vehicles has gradually attracted attention. Effective evaluation of autonomous vehicles can ensure the safety of autonomous vehicles and improve their level of intelligence. The current related research focuses on the evaluation of vehicles in terms of safety and comfort, while there are few studies on the evaluation of the learning ability of autonomous vehicles from the time dimension. Therefore, this article proposes three learning indicators: learning time, learning speed and learning capacity. These three indicators are used to measure the learning ability of autonomous driving algorithms. By analyzing the learning curve of the vehicle algorithm, the evaluation results of three learning indicators can be obtained. Thus the learning ability of the automatic driving algorithm can be evaluated. The differences in different vehicle algorithms can also be compared. In this article, taking the parking algorithm based on reinforcement learning as an example, simulation examples and analysis of the three learning indicators proposed are carried out.