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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.

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Racecar drivers are skilled at tracking a path, avoiding accidents, and controlling their vehicles at the limits of handling. Better understanding of how a skilled driver selects and drives a racing line, could potentially lead to a new technique for obstacle avoidance. To investigate this, the characteristics of a racecar driver’s line must be captured mathematically. This paper describes an algorithm for fitting a path to the GPS data of a driver’s racing line. A family of path primitives composed of straights, clothoids, and constant radius arcs are used to describe the racing line. The fitted paths provide a method for analyzing racing lines and the different techniques used by skilled drivers to navigate the track.