Tube model predictive control for an autonomous race car

Wischnewski, Alexander; Euler, Marianna; Gumus, Serap; Lohmann, Boris

doi:10.1080/00423114.2021.1943461

Cited by 26 publications

(14 citation statements)

References 58 publications

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“…This reaches the constraints of the lateral deviation in the optimization problem of the MPC. If the lateral deviation increases to more than 1 m, the aggressiveness of the controller behavior increases significantly due to the controller design as proposed in Wischnewski et al (2021). A lateral deviation of >1 m is still manageable, but the risk increases disproportionately above this.…”

Section: Event Analysismentioning

confidence: 99%

“…This includes feed‐forward as well as feedback actions. The controller is structured as a three‐layer concept (Figure 8a), with the highest layer utilizing a Tube‐MPC with a limited friction point‐mass model, an extension of a previous work (Wischnewski et al, 2021). This layer handles deviations in the position and velocity.…”

Section: Tum Autonomous Motorsport Softwarementioning

confidence: 99%

See 1 more Smart Citation

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz

Fent

et al. 2023

Journal of Field Robotics

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For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single‐vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 2700.25em km h − 1 $270\,\text{km\hspace{0.05em}h}{}^{-1}$ and 280.25em m s − 2 $28\,ms{}^{-2}$. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2‐year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real‐world evaluation of the displayed concepts.

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Section: Event Analysismentioning

confidence: 99%

Section: Tum Autonomous Motorsport Softwarementioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz

Fent

et al. 2023

Journal of Field Robotics

View full text Add to dashboard Cite

show abstract

“…This includes feed-forward as well as feedback actions. The controller is structured as a three-layer concept (Figure 8a), with the highest layer utilizing a Tube-MPC with a limited friction point-mass model, an extension of a previous work (Wischnewski et al, 2021). This layer handles deviations in the position and velocity.…”

Section: Motion Controlmentioning

confidence: 99%

“…If the lateral deviation increases to more than 1 m, the aggressiveness of the controller behavior increases significantly due to the controller design as proposed in (Wischnewski et al, 2021). A lateral deviation of > 1 m is still manageable, but the risk increases disproportionately above this.…”

Section: Indianapolis -Indy Autonomous Challengementioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz¹,

Betz²,

Fent³

et al. 2022

Preprint

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For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems like disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the Indy Autonomous Challenge, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from TUM placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 270 km h −1 and 28 m s −2 . This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the two-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. Based on this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts.

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“…Stability and recursive feasibility guarantees are possible if the uncertainty bound is known initially. RMPC has successfully been applied to safety-critical applications such as automated driving [4], autonomous racing [5], and robotic manipulation [6].…”

Section: Introductionmentioning

confidence: 99%

Safe Stochastic Model Predictive Control

Brüdigam¹,

Robert²,

Wollherr³

et al. 2022

Preprint

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Combining efficient and safe control for safetycritical systems is challenging. Robust methods may be overly conservative, whereas probabilistic controllers require a tradeoff between efficiency and safety. In this work, we propose a safety algorithm that is compatible with any stochastic Model Predictive Control method for linear systems with additive uncertainty and polytopic constraints. This safety algorithm allows to use the optimistic control inputs of stochastic Model Predictive Control as long as a safe backup planner can ensure safety with respect to satisfying hard constraints subject to bounded uncertainty. Besides ensuring safe behavior, the proposed stochastic Model Predictive Control algorithm guarantees recursive feasibility and input-to-state stability of the system origin. The benefits of the safe stochastic Model Predictive Control algorithm are demonstrated in a numerical simulation, highlighting the advantages compared to purely robust or stochastic predictive controllers.

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Tube model predictive control for an autonomous race car

Cited by 26 publications

References 58 publications

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Safe Stochastic Model Predictive Control

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