Abstract
Recently, autonomous driving has become one of the most popular topics for smart vehicles. However, traditional control strategies are mostly rule-based, which have poor adaptability to the time-varying traffic conditions. Similarly, they have difficulty coping with unexpected situations that may occur any time in the real-world environment. Hence, in this paper, we exploited Deep Reinforcement Learning (DRL) to enhance the quality and safety of autonomous driving control. Based on the road scenes and self-driving simulation modules provided by AirSim, we used the Deep Deterministic Policy Gradient (DDPG) and Recurrent Deterministic Policy Gradient (RDPG) algorithms, combined with the Convolutional Neural Network (CNN), to realize the autonomous driving control of self-driving cars. In particular, by using the real-time images of the road provided by AirSim as the training data, we carefully formulated an appropriate reward-generation method to improve the convergence speed of the adopted DDPG and RDPG models and the control performance of moving driverless cars.
Highlights
During the past decade, there have been many use cases for artificial intelligence in smart vehicles in our lives, e.g., convenience, exploration, rescue, and so on
Rule-based control strategies are unfit for the time-varying traffic conditions, and they have difficulty coping with unexpected traffic situations in the real-world environment
Some control strategies are implemented based on the absolute positioning information from the Global Positioning System (GPS), and this may result in precision and availability problems, that is, for some scenarios, GPS may not always be precise and available because of the effects of signal attenuation and multipath propagation [1,2,3]
Summary
There have been many use cases for artificial intelligence in smart vehicles in our lives, e.g., convenience, exploration, rescue, and so on. Our work is the sole research work to consider both camera vision and distance information based on different methods simultaneously for a simulated vehicle model in order to present a comprehensive discussion. We used a significant procedure to properly deal with the camera vision before importing it into the training and testing procedures instead of retrieving the information from the simulation environment directly This helped us improve the portability of our research on our autonomous control strategy to a real vehicle in a real-world environment later. In [3,8,9], the vision information captured from the simulation software was utilized directly This may reduce the level of confidence while realizing the designs in a real-world environment. Notice that AirSim supports many sensors, including cameras, Inertial Measurement Units (IMUs), GPS, distance sensors, LiDAR, and so forth, which can be configured for distinct scenarios and applications
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