End-to-End Adaptive Cruise Control Based on Timing Network

In recent years, driverless vehicle technology receives more attention because of its excellent performance on safety and efficiency. On the other hand, driverless vehicle calls for high-precision environmental perception and expert-like control strategies, which needs both lots of costly sensors and complex algorithms, and makes it difficult to achieve. Machine learning provides a new theoretical basis to solve this problem with big data, while most of data has not been calibrated yet. To solve these problems partly, a machine learning model based on a temporal neural network is described in this paper to achieve “end-to-end” self-driving from uncalibrated monocular images to control signals. The proposed approach is designed for adaptive cruise control situation. The approach is implemented in a simulation platform which has the control signal data from “expert.” According to the experiment in simulation platform, it shows that the proposed approach achieves “end-to-end” self-driving and has good performance on the prediction of desired acceleration.