Abstract
The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. It was chosen to implement a local trajectory planning based on the Model Predictive Control(MPC) logic and solved as Linear Programming (LP) by Sequential Convex Programming (SCP). The idea was to achieve a computational cost, 0.1 s, using a point mass vehicle model constrained by experimental definition and approximation of the car’s GG-V, and developing an optimum model-based path tracking to define the driver model that allows A car to follow the trajectory defined by the planner ensuring a signal input every 0.001 s. To validate the algorithm, two types of tests were carried out: a Matlab-Simulink, Vi-Grade co-simulation test, comparing the proposed algorithm with the performance of an offline motion planning, and a real-time simulator test, comparing the proposed algorithm with the performance of a professional driver. The results obtained showed that the computational cost of the optimization algorithm developed is below the limit of 0.1 s, and the architecture showed a reduction of the lap time of about 1 s compared to the offline optimizer and reproducibility of the performance obtained by the driver.
Highlights
The test is composed of two phase: the first one involved co-simulation between the trajectory tracking developed in Matlab-Simulink and the car model of the dynamic simulation software CRT; and the second one involved the minimum curvature optimization of Calabogie racetrack and the execution of the max-performance event explained in more detail in the subsection
We developed an autonomous racing car trajectory tracker that was able to update the optimized trajectory online to reduce the time lap as well as control the car, and we validated this by comparing it with the performance of offline path planning and a human driver
The intention of the paper was to ensure the online functionality of an autonomous controller to match the decisions of a human driver instead of optimizing the trajectory offline considering a possible development for an urban area. This aim was achieved by dividing the path planning algorithm from the trajectory control one and building a hierarchical control architecture, where the vehicle model complexity increased and the sample time decreased, i.e., the path planning uses a point mass vehicle model with a sample time of 0.1 s, and the path tracking uses a singletrack vehicle model with a sample time of 0.001 s
Summary
Research on the automotive field has focused on making cars more and more able to make decisions autonomously due to the growth of electric and electronic technologies on modern road cars and to the possibility of providing increased safety and improved performance. The trajectory planning was embedded with a path tracking consisting of a Linear Quadratic Regulator (LQR) that provides the car’s input signals: steering wheel angle, accelerator and brake pedals signals In this way, even if in a racing world scenario, real-time trajectory optimization can ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. Some authors preferred to develop a geometrical solution by minimizing the curvature [8] or generating a racing line using professional driving techniques [10] These techniques do not provide information about vehicle dynamics, despite the fact that the trajectory of least curvature ensures lap similar to the trajectory optimization techniques [8]. The tests performed will provide the results obtained by the algorithm in terms of the car’s trajectory and lap time, comparing them with those obtained by the CarRealTime (CRT) MaxPerformance event, and by a professional driver in a real-time car simulator
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