Generating a Racing Line for an Autonomous Racecar Using Professional Driving Techniques

Abstract

During a race, professional drivers follow a racing line using specific maneuvers that allow them to utilize as much of the car’s tire force as possible. These lines could be used to create trajectories for obstacle avoidance in autonomous vehicles if they could be analytically defined. In fact, many of the techniques described by professional drivers can be expressed by a family of simple curves including straights, clothoids, and constant radius arcs. By comparing different members of this family of curves, different racing techniques can be examined. In particular, the differences between two phase and three phase corners described by professional drivers can be easily captured and analyzed in a single parameter. Experimental results on an autonomous race-car highlight the advantages of two phase cornering over three phase cornering and demonstrate the types of comparisons that can be made with this approach.

A Sequential Two-Step Algorithm for Fast Generation of Vehicle Racing Trajectories

“…7. To validate the proposed algorithm, the racing line is compared with results from a nonlinear gradient descent algorithm implemented by Theodosis and Gerdes [5] and an experimental trajectory recorded from a professional racecar driver in the testbed vehicle ( Fig. 6).…”

“…More recently, the development of autonomous vehicle technology at the industry and academic level has led to research on optimal path planning algorithms that can be used for driverless cars. Theodosis and Gerdes published a gradient descent approach for determining time-optimal racing lines, with the racing line constrained to be composed of a fixed number of clothoid segments [5]. Given the computational expense of performing nonlinear optimization, there has also been a significant research effort to find approximate methods that provide fast lap times.…”

Path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control

“…1)c. This paper focuses on learning the desired steer angle command δ L over multiple laps in order to accurately track the reference path at high speeds, and we therefore assume minimum time velocity and curvature profiles have been computed using methods published in [8], and that tracking of the velocity profile is handled by a separate controller [9]. Fig.…”

Design of a feedback-feedforward steering controller for accurate path tracking and stability at the limits of handling

“…The curvature profile associated with the racing line is shown in Figure 13 along with a typical longitudinal velocity profile U x from the path planner. [13] …”

“…[13] Additionally, the velocity profile is obtained from a longitudinal controller that keeps the combined vehicle acceleration magnitude at a specified value. [7] …”