Abstract
Urban complex scenarios are the most challenging situations in the field of Autonomous Driving (AD). In that sense, an AD pipeline should be tested in countless environments and scenarios, escalating the cost and development time exponentially with a physical approach. In this paper we present a validation of our fully-autonomous driving architecture using the NHTSA (National Highway Traffic Safety Administration) protocol in the CARLA simulator, focusing on the analysis of our decision-making module, based on Hierarchical Interpreted Binary Petri Nets (HIBPN). First, the paper states the importance of using hyper-realistic simulators, as a preliminary help to real test, as well as an appropriate design of the traffic scenarios as the two current keys to build safe and robust AD technology. Second, our pipeline is introduced, which exploits the concepts of standard communication in robotics using the Robot Operating System (ROS) and the Docker approach to provide the system with isolation, flexibility and portability, describing the main modules and approaches to perform the navigation. Third, the CARLA simulator is described, outlining the steps carried out to merge our architecture with the simulator and the advantages to create ad-hoc driving scenarios for use cases validation instead of just modular evaluation. Finally, the architecture is validated using some challenging driving scenarios such as Pedestrian Crossing, Stop, Adaptive Cruise Control (ACC) and Unexpected Pedestrian. Some qualitative (video files: Simulation Use Cases) and quantitative (linear velocity and trajectory splitted in the corresponding HIBPN states) results are presented for each use case, as well as an analysis of the temporal graphs associated to the Vulnerable Road Users (VRU) cases, validating our architecture in simulation as a preliminary stage before implementing it in our real autonomous electric car.
Highlights
According to the World Health Organization, nearly one third of the world population will live in cities by 2030, leading to an overpopulation in most of them
We hope that our distributed system can serve as a solid baseline on which future research can build on to advance the state-of-the-art in validating fully-autonomous driving architectures using hyper-realistic simulation, as a preliminary stage before implementing our architecture in our real electric car prototype
This work presents the validation of our Robot Operating System (ROS)-based fully-autonomous driving architecture, focusing in the decision-making layer, with CARLA, a hyper-realistic, real-time, flexible and open-source simulator for autonomous vehicles
Summary
According to the World Health Organization, nearly one third of the world population will live in cities by 2030, leading to an overpopulation in most of them. The online information, known as the traffic situation, is obtained through the use of a global perception system of the vehicle, which involves different on-board sensors as: Inertial Measurement Unit (IMU), Light Detection And Ranging (LiDAR), RAdio Detection And Ranging (RADAR), Differential-Global Navigation Satellite System (D-GNSS), Wheel odometers or Cameras. For this purpose, a self-driving car must sense its environment and moving safely with little or even no human intervention.
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