End-to-End Time Distributed Convolution Neural Network Model for Self Driving Car in Moderate Dense Environment
Vehicle control in Autonomous Car requires the following command to make sure that the car can accomplish a specific task, such as taking a turn, stop on the traffic light, following lanes, and changing lanes. This serial command indicates that a self-driving car should not be addressed as a context-based problem that theoretically needs a temporal system that can accommodate multiple frames.
Based on this added complexity of the problem, we propose a network that can accommodate the sequential input of images. Thus, we apply a time distributed model of Convolutional Neural Network (CNN), to recognize a visual problem, followed by LSTM that can capture temporal state dependencies.
By modifying the Carla environment, we can capture frame per frame images with detailed information of throttling, speed, steering angle, brake, and some states such as direction, speed limit, and traffic light state. We use the Carla control agent so that it can automatically capture all of the images from the camera and those of information. We demonstrate that this rough approach can perform well in the Carla environment with moderate dense traffic. It can reach the destination faster than the ground truth and standard convolution model in just 93.978 seconds. Although the driver agent performance is a bit rough with around 13.27 of speed above score, it shows a better steering control, which means better stability.
Keywords: Time Distributed, LSTM, CNN, Carla, Autonomous Car
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