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Abstract
Automated Driving Systems (ADSs) require robust and scalable control systems in order to achieve a safe, efficient and comfortable driving experience. Most global planners for autonomous vehicles provide as output a sequence of waypoints to be followed. This paper proposes a modular and scalable waypoint tracking controller for Robot Operating System (ROS)-based autonomous guided vehicles. The proposed controller performs a smooth interpolation of the waypoints and uses optimal control techniques to ensure robust trajectory tracking even at high speeds in urban environments (up to 50 km/h). The delays in the localization system and actuators are compensated in the control loop to stabilize the system. Forward velocity is adapted to path characteristics using a velocity profiler. The controller has been implemented as an ROS package providing scalability and exportability to the system in order to be used with a wide variety of simulators and real vehicles. We show the results of this controller using the novel and hyper realistic CARLA Simulator and carrying out a comparison with other standard and state-of-art trajectory tracking controllers.
Highlights
In recent years, both the research community and companies have paid special attention to the topic of autonomous driving
Since this baseline control does not incorporate the velocity profiler, the trajectory is made at constant speed
Baseline control does not incorporate the velocity profiler, the trajectory is made at constant speed
Summary
Both the research community and companies have paid special attention to the topic of autonomous driving. A good part of these research efforts focuses on perception and interpretation of the complex and dynamic environment that includes other vehicles, traffic signaling, urban infrastructure and pedestrians [1,2] These efforts would be useless without a robust planning and control system that allows a safe and fast execution of the planned movements, through convenient feedback control loops. The great computation power of current onboard processors has leveraged research in machine and deep learning methods in all ADS modules, including planning and control [3,4] The advantage of these control methods is their ability to drive in the absence of maps, relying on a comprehensive understanding of the surrounding environment while following high level learnt commands. These methods require arduous and specific training for each environment, and the results in the field of trajectory tracking control are not yet robust or reliable
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