Discovery Sagaサイレントキーワード俯瞰

This project aims to investigate chassis dynamics domain control for automated vehicles (AVs). A decision-making module provides proper driving commands for chassis actuators to track, so the first step of studying chassis domain control is to design or employ a decision-making module. Here we have investigated a convolutional neural network-based approach to generate both longitudinal and lateral driving maneuver commands.
This project has established a simulation platform based on the simulation of urban mobility (SUMO) to collect driving data. Then, a spatio-temporal image representation approach was proposed to depict full-scale traffic information in motion-sensitive area around the ego car. After that, bulks of motion images together with driving maneuver data were collected and divided into training and test dataset based on the SUMO platform. We have designed a network architecture with the convolutional neural networks and long short-term memory layers to extract both spatial and sequential traffic representations and learn underlying driving operation features. The deep-learning-based decision-making method has been trained and tested by the collected datasets from SUMO, and the current results validate the effectiveness of the proposed scheme. This part of research work has been submitted to IFAC Mechatronics -MoViC2022 Conference.