Authors:
Qazi Jan
;
Arshil Khan
and
Karsten Berns
Affiliation:
RPTU Kaiserslautern-Landau, Erwin-Schrödinger-Straße 52, 67663, Kaiserslautern, Germany
Keyword(s):
Autonomous Driving, Pedestrian Zones, End-to-End Driving.
Abstract:
With the development of machine learning techniques and increase in their precision, they are used in different aspects of autonomous driving. One application is end-to-end driving. This approach directly takes in the sensor data and outputs the control value of the vehicle. End-to-end systems have widely been used. The goal of this work is to investigate the effect of change in weather condition, presence of pedestrians, and reason the prediction failure, along with improving the results in a pedestrian zone. Driving through the pedestrian zone is challenging due to the narrow path and crowd of people. This work uses RGB images from a front-facing camera mounted on the roof of a minibus and outputs the steering angle of the vehicle. A Convolutional Neural Network (CNN) is implemented for regression prediction. The testing was first done in a simulation environment which comprised of the replicated version of the campus, the sensor system and the vehicle model. Thorough testing is do
ne in different weather conditions and with the simulated pedestrians to check the robustness of the system for such diversified changes in the environment. The vehicle avoided the simulated pedestrians placed randomly at the boundary of narrow paths. In an unseen environment, the vehicle approached the region with the same texture it was trained on. Later, the system was transferred to a real machine and further trained and tested. Due to unavailability of the ground truth, the results can not be delineated for real world testing, but are reasoned through visual monitoring. The vehicle followed the path and performed well in an unseen environment as well.
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