Development of objective criteria to assess the vehicle performance utilized by the driver in near-limit handling conditions of racecars

Goy, Florian; Wiedemann, Jochen; Völkl, T.; Colli, Gustavo; Neubeck, Jens; Krantz, Werner

doi:10.1007/978-3-658-13255-2_90

Cited by 1 publication

(3 citation statements)

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“…Common motorsport metrics such as lap time, top speed or accelerations provide performance measures but do not give insights how they were achieved nor why certain traits are observed. Also properties which are linked to handling and therefore to driveability are difficult to extract from these metrics [10]. Autonomous race driving is an area where understanding of vehicle handling at the limit is crucial.…”

Section: Related Workmentioning

confidence: 99%

“…L( ẋ), c( ẋ) and ceq( ẋ) can be nonlinear functions [23]. For the problem specified in this work, ceq( ẋ) contains the vehicle model's momentum equalities (3,4,5) as well as equality constraints (7,8,9,10). The inequalities contained in c( ẋ) are described by equations (11,12,13).…”

Section: Optcog Optimisationmentioning

confidence: 99%

“…The combination of the boolean variables results in the four control states. These relate to the commonly defined cornering sections [10], which are in parentheses:…”

Section: Control Statesmentioning

confidence: 99%

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Race Driver Evaluation at a Driving Simulator using a physical Model and a Machine Learning Approach

Schleinitz¹,

Schwarzhuber²,

Wörle³

et al. 2022

Preprint

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Professional race drivers are still superior to automated systems at controlling a vehicle at its dynamic limit. Gaining insight into race drivers' vehicle handling process might lead to further development in the areas of automated driving systems. We present a method to study and evaluate race drivers on a driver-in-the-loop simulator by analysing tire grip potential exploitation. Given initial data from a simulator run, two optimiser based on physical models maximise the horizontal vehicle acceleration or the tire forces, respectively. An overall performance score, a vehicle-trajectory score and a handling score are introduced to evaluate drivers. Our method is thereby completely track independent and can be used from one single corner up to a large data set. We apply the proposed method to a motorsport data set containing over 1200 laps from seven professional race drivers and two amateur drivers whose lap times are 10 − 20% slower. The difference to the professional drivers comes mainly from their inferior handling skills and not their choice of driving line. A downside of the presented method for certain applications is an extensive computation time. Therefore, we propose a Long-short-term memory (LSTM) neural network to estimate the driver evaluation scores. We show that the neural network is accurate and robust with a root-mean-square error between 2 − 5% and can replace the optimisation based method. The time for processing the data set considered in this work is reduced from 68 hours to 12 seconds, making the neural network suitable for real-time application.

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Section: Related Workmentioning

confidence: 99%