Extended Steady State Lap Time Simulation for Analyzing Transient Vehicle Behavior

Völkl, Timo; Muehlmeier, Martin; Winner, Hermann

doi:10.4271/2013-01-0806

Cited by 19 publications

(18 citation statements)

References 8 publications

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“…Differentiating with respect to time, Equations (17) and (18) can be simplified to give each wheel acceleration constraint:…”

Section: Generating the Gg Diagrammentioning

confidence: 99%

“…More recently, quasi-transient approximations have been developed, [16,17] which allow the inclusion of a number of parameters including yaw acceleration, vertical suspension damping ratio and tyre temperature. It is important to note that all current methods aim to predict purely the open loop performance limits of the vehicle, not the driver's ability to use them.…”

Section: Generating the Gg Diagrammentioning

confidence: 99%

See 1 more Smart Citation

Quasi-steady-state linearisation of the racing vehicle acceleration envelope: a limited slip differential example

Tremlett

Assadian

Purdy

et al. 2014

Vehicle System Dynamics

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In the motorsport environment, passive limited slip differentials are a well-established means of improving the traction limitation imposed by the open differential. Torque sensing types are highly adjustable, and can alter both the stability and agility of the vehicle in the various cornering phases of a typical manoeuvre. In this paper, an adjustable clutch plate or 'Salisbury' differential model is presented, which can significantly alter its torque bias characteristics through adjustments in the drive/coast ramp angle, the number of friction faces and preload. To allow robust evaluation of differential parameter changes on ultimate vehicle performance and handling balance, a unified acceleration or 'GG' diagram framework is then described. This builds on traditional GG diagram approaches, by using nonlinear constrained optimisation to define both the vehicle acceleration limits and a 'feasibility' region within the performance envelope. By linearising a seven degrees of freedom vehicle model at multiple operating points, eigenvalue and yaw rate response analysis is then used to establish contours of stability and agility throughout the GG envelope. This brings new insights into the way in which handling balance changes below and up to the vehicle's acceleration limits.

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“…Differentiating with respect to time, Equations (17) and (18) can be simplified to give each wheel acceleration constraint:…”

Section: Generating the Gg Diagrammentioning

confidence: 99%

Section: Generating the Gg Diagrammentioning

confidence: 99%

Quasi-steady-state linearisation of the racing vehicle acceleration envelope: a limited slip differential example

Tremlett

Assadian

Purdy

et al. 2014

Vehicle System Dynamics

View full text Add to dashboard Cite

show abstract

“…Minimum-lap-time problems have been historically solved by employing quasi-steadystate models on a predetermined (fixed) trajectory, see e.g. [1][2][3][4][5][6][7][8][9], or by employing dynamic models on a free (i.e. not predetermined) trajectory, see e.g.…”

Section: Introductionmentioning

confidence: 99%

A three-dimensional free-trajectory quasi-steady-state optimal-control method for minimum-lap-time of race vehicles

Lovato

Massaro

2021

Vehicle System Dynamics

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A method to solve minimum-lap-time problems using quasi-steady-state models and free (i.e. not predetermined) trajectory on two-dimensional tracks has been recently proposed. This work describes the extension of the method to three-dimensional tracks and builds upon g-g-g diagrams (instead of the standard g-g), to account for the effects of three-dimensionality. The main features of car and motorcycle g-g-g diagrams are discussed, to get insight into the main effects of three-dimensionality and to suggest a convenient parametrisation for the subsequent optimal control problem (OCP), whose size is not affected by the complexity of the vehicle model employed to generate the g-g-g diagrams. The application of the method to the Mugello and Barcelona-Catalunya circuits is given, with vehicle datasets resembling those of a race motorcycle (MotoGP) and a race car (Formula One). The results obtained are in line with those reported in the literature using full-dynamic models, yet such dynamic models are generally associated to the solution of much larger OCP.

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“…The majority of lap-time simulators in use by race teams are based around Quasi-Steady-State (QSS) approaches (Dal Bianco et al, 2018) -where a vehicle model follows a target path (typically discretised into a series of curves of varying radii) at the highest possible speed, obtained through iteration. In more advanced cases this method can be further extended to incorporate transient vehicle behaviour (Völkl et al, 2013). The QSS approach requires pre-existing knowledge of a target path, which is usually obtained from logged data of the line taken on the fastest lap driven by a professional racing driver (Colunga and Bradley, 2014) -however, this assumes that the driver took the correct line in the first place, and indeed that it is possible for a human to physically drive the vehicle on the circuit in advance of the race.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning

Garlick¹,

Bradley²

2021

Preprint

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The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsport provide a testbed to accelerate technology development. A particular challenge for an autonomous vehicle is that of identifying a target trajectory -or in the case of a racing car, the ideal racing line. Many existing approaches to identifying the racing line are either not the time-optimal solutions, or have solution times which are computationally expensive, thus rendering them unsuitable for real-time application using on-board processing hardware. This paper describes a machine learning approach to generating an accurate prediction of the racing line in real-time on desktop processing hardware. The proposed algorithm is a dense feed-forward neural network, trained using a dataset comprising racing lines for a large number of circuits calculated via a traditional optimal control lap time simulation. The network is capable of predicting the racing line with a mean absolute error of ±0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system. The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line. Results suggest that a data-driven approach may therefore be favourable for real-time generation of nearoptimal racing lines than traditional computational methods.

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Extended Steady State Lap Time Simulation for Analyzing Transient Vehicle Behavior

Cited by 19 publications

References 8 publications

Quasi-steady-state linearisation of the racing vehicle acceleration envelope: a limited slip differential example

Quasi-steady-state linearisation of the racing vehicle acceleration envelope: a limited slip differential example

A three-dimensional free-trajectory quasi-steady-state optimal-control method for minimum-lap-time of race vehicles

Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning

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