LLA-MPC: Fast Adaptive Control for Autonomous Racing

Hassan Almubarak; John M. Dolan; Katherine Horng; Maitham F. AL-Sunni

REVIEW 5 major objections 5 minor 1 cited by

LLA-MPC: Fast Adaptive Control for Autonomous Racing

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that a learning-free model-bank controller, selected by recent prediction error, adapts to sudden tire-friction changes in real time and beats a trained baseline in simulated racing.

desk verdict A useful learning-free adaptive racing controller, but the central claims rest on a model bank that knows the truth and a real-time figure that doesn't add up. read the letter →

arxiv 2505.19512 v1 pith:TS3JZ7XU submitted 2025-05-26 cs.RO cs.SYeess.SY

Maitham F. AL-Sunni , Hassan Almubarak , Katherine Horng , John M. Dolan This is my paper · ORCID

classification cs.ROcs.SYeess.SY

keywords autonomousracingadaptiveMPCmodelbankfrictionestimationtire-roadinteractionlook-backselectionreal-timecontrolmulti-surfacedriving

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes a controller for autonomous racing that adapts to changing tire–road friction without any training or data-collection phase. Instead of learning a model, it keeps a large bank of physics-based bicycle models sampled from plausible parameter ranges, and at each step selects the model that best predicted recent past states (the look-back window). That selected model and a friction estimate derived from its tire parameters feed a look-ahead MPC and an adaptive speed planner. In simulations on two miniature-car tracks and in CARLA, the method matches or beats a neural-network baseline across gradual and sudden friction changes, with lower lap times, fewer track violations, and lower computation time.

What carries the argument

The load-bearing object is the model bank: a set of $N$ dynamic bicycle models, each with tire and resistance parameters $\theta = [B_f, B_r, C_f, C_r, D_f, D_r, C_{ro}, C_d]$ sampled uniformly from ranges bracketing the expected values. The selection rule is the look-back window: at each time step, the model minimizing the accumulated squared one-step prediction error over the past $W$ steps is chosen (Eqs. 5–7), and that model drives the look-ahead MPC (Eq. 8). The friction coefficient $\mu$ is then computed from the selected model's peak lateral forces and exponentially smoothed (Eqs. 11–12) to pick a speed profile from a precomputed library. The paper argues that the diversity of the bank plus the short window gives immediate adaptation, and supports this by showing MPC cost decreasing as $N$ grows.

What would settle it

Run the same ETHZ and ETHZMobil experiments but set the true friction coefficient or tire stiffness to a value outside the plus-or-minus 150% range used to build the bank, and check whether the selected model and estimated $\mu$ track the new value; if LLA-MPC fails or degrades to the boundary model, the bank-coverage premise is refuted.

Watch

Extended reading notes

Core claim

The paper's central claim is that a learning-free model-selection scheme can outperform learned adaptive controllers for high-speed racing under rapidly changing grip. Concretely, LLA-MPC samples a bank of dynamic bicycle models with tire-force equations, evaluates their one-step-ahead predictive error over a short sliding window, picks the best model, and uses it both in an MPC and to estimate the road friction coefficient. In the tested scenarios—gradual friction decay, a sudden 40% drop after lap 1, and the same drop early in lap 1—the method tracks the Oracle controller closely, whereas the APACRace baseline takes longer to adapt and goes off-track in the early-drop case. The paper reports that LLA-MPC requires no initial data-collection interval and runs at about half the per-iteration compute time of the baseline.

Load-bearing premise

The bank's parameter ranges must contain the true vehicle and tire parameters; in the experiments these ranges are centered on the simulator's ground-truth values, so the paper never tests what happens when the true system lies outside the bank.

Editorial extensions

If this is right

LLA-MPC can begin racing at full performance immediately, with no warm-up laps, because model selection starts after a window of only 0.2 s.
Sudden grip changes as large as a 40% friction drop are handled within a fraction of a lap, with lap times close to an oracle that knows the friction in advance.
The per-iteration computation time (about 0.03 s for $N=20000$) stays within real-time budgets for the tested miniature-car platform.
Because the framework only requires that each model predict the same state space, the bank could mix kinematic, dynamic, or data-driven structures without changing the selection logic.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Inference: If the bank-coverage assumption holds in reality, the same selection scheme could be applied to other fast-changing parameters, such as mass distribution or aerodynamic drag, without retraining.
Inference: A natural stress test the paper leaves open is to run the true system outside the bank's parameter box; the method would then likely select a boundary model, and the friction estimate would be visibly biased.
Inference: The look-back window length $W$ and bank size $N$ interact with the sampling distribution; uniform sampling may be suboptimal when parameters are correlated, and adaptive sampling could improve coverage per compute.
Inference: Because the framework is learning-free and parallelizable, it is a candidate for embedded deployment where training infrastructure is unavailable, provided parameter ranges can be specified a priori.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper applies multiple-model adaptive control in the style of Narendra and Balakrishnan to autonomous racing, using a bank of dynamic bicycle models sampled over tire and friction parameter ranges. The look-back window picks the model with the best recent one-step prediction error, that model drives an MPC, and the selected D parameters give a friction estimate that selects a velocity profile. The system is learning-free and runs without a training phase, and the authors show in simulation that it handles gradual and sudden friction changes better than the closest prior art (APACRace). That is a genuinely useful integration, and credit goes to the authors for keeping the method simple, parallelizable, and for reporting compute times and bank-size sweeps.

The soft spots are significant. The model bank is sampled from bounds centered on the true simulator parameters plus/minus 150%, so the true system is inside the bank by construction. The friction estimate is a direct function of the selected model's D parameters, so the 'estimate' is essentially a lookup from a bank that already contains the answer. The paper never runs the case where the true parameters are outside the bank, which is the case a real system would present. The experiments use one baseline, no error bars or statistical tests, and CARLA results are only on a website. And there is a concrete real-time inconsistency: the paper sets the control sampling time to 20 ms but reports average per-iteration computation time of 30 ms for LLA-MPC, which is slower than the loop time. That cannot be a real-time implementation unless the simulation ignores computation delay.

These are not fatal to the underlying idea—the method is coherent and the mechanisms are transparent—but they are load-bearing flaws in the central claim of outperforming state-of-the-art methods. A revision with out-of-bank tests, multiple baselines, error bars, and an honest real-time accounting would make the paper solid.

Who is this for? Researchers working on adaptive MPC for racing or fast systems who want a learning-free alternative to data-driven identification. It deserves a serious referee: the idea is worth engaging with, and the right reviewer can push the authors to fix the validation. I'd accept it for review but expect heavy revision.

Referee Report

5 major / 5 minor

Summary. The paper proposes LLA-MPC, a learning-free adaptive model predictive control framework for autonomous racing. The method constructs a bank of dynamic bicycle models with tire and drag parameters sampled uniformly from a specified range, selects the best model at each time step via a sliding window of one-step-ahead prediction errors, and uses the selected model both in an MPC planner and to estimate the road friction coefficient, which in turn selects a reference speed profile. Experiments are reported in a low-fidelity 1:43-scale numerical simulator and in CARLA, comparing against APACRace and an Oracle controller. The central claims are that LLA-MPC adapts immediately without training data, operates in real time, and outperforms state-of-the-art adaptive on-track methods in lap time, safety, tracking, and computational cost.

Significance. If the claims were fully substantiated, the paper would make a useful contribution: a model-bank selection scheme that requires no offline training and adapts to abrupt friction changes would be attractive for autonomous racing and for adaptive MPC more broadly. The paper is clearly written, the idea is simple and plausible, and the authors provide code and video links. However, the empirical evidence as presented does not currently support the headline claims. The real-time claim is contradicted by the paper's own compute-time numbers; the friction-estimation claim is tested only in the favorable case where the model bank is constructed around the true parameter values; the CARLA results are not in the manuscript; and the performance comparisons lack any statistical characterization. These are central, not peripheral, weaknesses.

major comments (5)

[Section IV.A, Tables II and III] The reported average per-iteration computation time of 0.03 s for LLA-MPC (both N = 20K and N = 10K) exceeds the sampling time ΔT = 0.02 s used in the experiments. A controller that requires 30 ms to produce an input cannot close a 20 ms control loop unless inputs are applied with zero computation delay, which the paper does not state. This contradicts the 'Real-time Capable ✓' entry in Table I and the abstract's claim of a 'real-time adaptive control framework.' Please clarify how the simulator handles the computation time, or revise the real-time claims accordingly.
[Section IV.A and Eq. (11)] The model bank is built by uniformly sampling from parameter ranges whose lower and upper bounds are −150% and +150% of the true simulator parameter values (Section IV.A). The friction estimate in Eq. (11) is a direct function of the D parameters of the selected model, so the claim that LLA-MPC 'accurately estimates' friction is partly circular: in the reported experiments, the true parameters are guaranteed to be inside the bank by construction. The paper contains no experiment in which the true parameters lie outside the bank or in which the bank is systematically misspecified. Such an out-of-bank test is necessary to support the general adaptation claim, since a model-selection method cannot recover parameters that are not represented in the candidate set.
[Section IV.B] The CARLA simulation results are not reported in the manuscript; the text says only that 'we present the results of the CARLA simulations on the paper's website.' The conclusion that LLA-MPC 'significantly outperforms state-of-the-art methods' across diverse scenarios cannot be verified from the paper itself. Please include the CARLA metrics (lap times, violation time, mean deviation, and computation time) in the paper, or explicitly limit the performance claims to the 1:43-scale simulator experiments.
[Section II.B and Algorithm 1] There is an indexing inconsistency between Eq. (5) and Algorithm 1. Eq. (5) defines the one-step prediction error at time k as e_k^j = ||x_k − f^j(x_{k−1}, u_{k−1})||², which is correct. Algorithm 1, however, after computing x_{k+1}, assigns e_{k+1}^j using x_k and f^j(x_{k−1}, u_{k−1}) instead of x_{k+1} and f^j(x_k, u_k). If the implementation follows the pseudocode, the look-back selection would use stale predictions. Please correct the pseudocode and state exactly which error is used for model selection.
[Tables II and III] All numerical results are averaged over 10 runs, but no standard deviations, confidence intervals, or statistical tests are reported. Several claimed advantages over APACRace are small in absolute terms (e.g., lap-time differences of 0.1–0.2 s on the ETHZMobil track), and without variance information it is difficult to assess whether these differences are significant. Please report run-to-run variability and, where appropriate, perform a statistical comparison.

minor comments (5)

[Algorithm 1] The loop bound 'for k = 0 to T ∆T do' should be written as 'for k = 0 to T/∆T do' or with explicit discrete time indices.
[Section III.A] In the dynamic bicycle model equations, the terms −mg sin(p) and +mg sin(r) appear in the vx and vy dynamics, but the variables p and r are not defined in the notation list. Please define these angles and clarify their sign conventions.
[Section IV.A] The phrasing 'a lower bound θ that is −%150 of the actual parameters' is awkward; please state clearly that the bounds are ±150% relative to the true parameter values.
[Reference [23]] The reference to Heilmeier et al. is missing volume and page information; please complete the bibliographic details.
[Section II.B] The text says that for the short period k < W a nominal model is used, but it is not specified how the nominal model is chosen and whether it is also a member of the bank. Please clarify.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation; stated model-bank prior is a coverage assumption, not a hidden fit.

full rationale

The derivation chain is self-contained. The look-back selection (Eqs. 5-7) chooses among bank models using measured state errors; the MPC (Eq. 8) uses the selected model; the friction estimate (Eqs. 11-12) is an explicit function of the selected model's tire parameters. No predicted quantity is defined in terms of the quantity it is supposed to predict. The model bank is sampled from stated ranges (Section IV.A), and centering these ranges on the simulator's true parameters is a transparent evaluation assumption about coverage, not a hidden circular fit: the method still must select the right model from data, and the experiments compare against an external baseline (APACRace). The self-citations (e.g., [17], co-authored by J.M. Dolan) are used as a baseline and for a velocity-profile library; they are not invoked as a uniqueness theorem or to forbid alternatives, and the comparison is independently implementable. The reported compute time exceeding the sampling interval is a correctness/real-time validity concern, not a circularity issue.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central empirical claim rests on the model bank containing a sufficiently close model. This requires the parameter ranges to bracket the true values, which the experiments guarantee by construction. The method introduces no new physical entities; the model bank is an algorithmic device.

free parameters (6)

Parameter bounds theta_bar/theta_bar = ±150% of true simulator parameters
Section IV.A sets bounds from actual parameters; this is the main free choice and biases the evaluation.
Look-back window W = 0.2 s
Chosen by hand without sensitivity analysis beyond Fig. 3.
Look-ahead horizon H = 0.4 s
Design choice; no justification or sensitivity analysis.
Smoothing factor gamma = unspecified
Eq. 12 uses gamma but no value is reported.
Model bank size N = 20000 (also 10000)
Chosen by hand; larger N improves cost (Fig. 2) with computational tradeoff.
Initial friction estimate mu_0 = 1.0
Set for all experiments; could favor rapid convergence.

assumptions (5)

domain assumption The actual vehicle parameters lie within the pre-specified bank ranges.
Section IV.A sets ranges using true values; not justified in practice.
domain assumption Pacejka tire model with the given functional form is an adequate representation of the true vehicle dynamics throughout the race.
Section III.A; model mismatch beyond parameters is not considered.
ad hoc to paper One-step-ahead prediction error minimization selects a model whose D parameters yield an accurate friction estimate.
Section III.B; no proof that best prediction error implies correct Df+Dr.
domain assumption Parallel hardware can evaluate all N models and solve MPC within the 0.02 s sample time.
Section IV reports 0.02-0.03 s average computation, but no platform details or worst-case analysis.
domain assumption The velocity profile library from [17] transfers to the test tracks.
Section III.B adopts profiles without validating transfer.

how reviews work

0 comments

Cite this review

Pith. "Pith review of LLA-MPC: Fast Adaptive Control for Autonomous Racing." pith.science (2026). https://pith.science/paper/TS3JZ7XU

@misc{pith2026250519512,
  author       = {Pith},
  title        = {Pith review of: LLA-MPC: Fast Adaptive Control for Autonomous Racing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TS3JZ7XU}},
  note         = {Machine review of arXiv:2505.19512}
}

read the original abstract

We present Look-Back and Look-Ahead Adaptive Model Predictive Control (LLA-MPC), a real-time adaptive control framework for autonomous racing that addresses the challenge of rapidly changing tire-surface interactions. Unlike existing approaches requiring substantial data collection or offline training, LLA-MPC employs a model bank for immediate adaptation without a learning period. It integrates two key mechanisms: a look-back window that evaluates recent vehicle behavior to select the most accurate model and a look-ahead horizon that optimizes trajectory planning based on the identified dynamics. The selected model and estimated friction coefficient are then incorporated into a trajectory planner to optimize reference paths in real-time. Experiments across diverse racing scenarios demonstrate that LLA-MPC outperforms state-of-the-art methods in adaptation speed and handling, even during sudden friction transitions. Its learning-free, computationally efficient design enables rapid adaptation, making it ideal for high-speed autonomous racing in multi-surface environments.

Figures

Figures reproduced from arXiv: 2505.19512 by the authors.

**Figure 3.** Impact of size of the look-back window ( [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

**Figure 4.** Comparison of friction coefficient (µ) estimations over time for the ETHZ track. model bank provides immediate friction estimates, enabling rapid adaptation. This integration allows the system to respond to sudden friction changes without the need of data collection that might compromise safety or stability, maintaining performance even during rapid surface transitions. IV. RESULTS We perform various experiments o… view at source ↗

Figures from the paper (3 more)

**Figure 5.** Figure 5: (Experiment 1) Comparison of racing trajectories on the ETHZMobil track under progressive friction decay. (a) LLA-MPC (N = 20000) (b) APACRace (c) Oracle [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: (Experiment 2) Comparison of racing trajectories under a sudden friction drop on the ETHZ track. tion reduction earlier, within the first lap, before APACRace completes its training phase. This scenario further increases the challenge, as controllers must adapt immedia…

**Figure 7.** Figure 7: (Experiment 3) Comparison of racing trajectories under an early sudden friction drop on the ETHZ track. control methods in both adaptation speed and trajectory tracking in high-speed racing scenarios. Its learning-free nature and computational efficiency make it well-s…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Vehicle Prediction Model for Enhanced MPC Path Tracking in Formula Student Driverless
cs.RO 2026-06 unverdicted novelty 5.0 of 10

Hybrid model of kinematic bicycle, offline BLR and online SGPR yields up to 57% better prediction accuracy for MPC path tracking on a real Formula Student Driverless car.

Reference graph

Works this paper leans on

24 extracted references · 22 canonical work pages · cited by 1 Pith paper

[1]

Autonomous vehicles on the edge: A survey on autonomous vehicle racing,

J. Betz, H. Zheng, A. Liniger, et al., “Autonomous vehicles on the edge: A survey on autonomous vehicle racing,” IEEE Open Journal of Intelligent Transportation Systems , vol. 3, pp. 458–488, 2022

work page 2022
[2]

Optimization-based autonomous racing of 1: 43 scale rc cars,

A. Liniger, A. Domahidi, and M. Morari, “Optimization-based autonomous racing of 1: 43 scale rc cars,” Optimal Control Ap- plications and Methods , vol. 36, no. 5, pp. 628–647, 2015

work page 2015
[3]

Model parameter identifi- cation via a hyperparameter optimization scheme for autonomous racing systems,

H. Seong, C. Chung, and D. H. Shim, “Model parameter identifi- cation via a hyperparameter optimization scheme for autonomous racing systems,” IEEE Control Systems Letters , vol. 7, pp. 1652– 1657, 2023

work page 2023
[4]

Motion planning and control for multi vehicle autonomous racing at high speeds,

A. Raji, A. Liniger, A. Giove, et al., “Motion planning and control for multi vehicle autonomous racing at high speeds,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2022, pp. 2775–2782

work page 2022
[5]

Analysis and control of high sideslip manoeuvres,

C. V oser, R. Y . Hindiyeh, and J. C. Gerdes, “Analysis and control of high sideslip manoeuvres,” Vehicle System Dynamics , vol. 48, no. S1, pp. 317–336, 2010

2010
[6]

Model-and acceleration-based pursuit controller for high-performance autonomous racing,

J. Becker, N. Imholz, L. Schwarzenbach, E. Ghignone, N. Baumann, and M. Magno, “Model-and acceleration-based pursuit controller for high-performance autonomous racing,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , IEEE, 2023, pp. 5276–5283

work page 2023
[7]

Online adaptation of learned vehicle dynamics model with meta-learning approach,

Y . Tsuchiya, T. Balch, P. Drews, and G. Rosman, “Online adaptation of learned vehicle dynamics model with meta-learning approach,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , IEEE, 2024, pp. 802–809

work page 2024
[8]

Agile mobility with rapid online adaptation via meta-learning and uncertainty-aware mppi,

D. Kalaria, H. Xue, W. Xiao, T. Tao, G. Shi, and J. M. Dolan, “Agile mobility with rapid online adaptation via meta-learning and uncertainty-aware mppi,” in 2025 International Conference on Robotics and Automation (ICRA) , 2025

work page 2025

Show all 24 references

[9]

Anycar to anywhere: Learning universal dynamics model for agile and adaptive mobility,

W. Xiao, H. Xue, T. Tao, D. Kalaria, J. M. Dolan, and G. Shi, “Anycar to anywhere: Learning universal dynamics model for agile and adaptive mobility,” in 2025 International Conference on Robotics and Automation (ICRA) , 2025

2025
[10]

Ensemble gaussian processes for adaptive au- tonomous driving on multi-friction surfaces,

T. Nagy, A. Amine, T. X. Nghiem, U. Rosolia, Z. Zang, and R. Mangharam, “Ensemble gaussian processes for adaptive au- tonomous driving on multi-friction surfaces,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 494–500, 2023

2023
[11]

Bayesrace: Learning to race autonomously using prior experience,

A. Jain, M. O’Kelly, P. Chaudhari, and M. Morari, “Bayesrace: Learning to race autonomously using prior experience,” in Confer- ence on Robot Learning , PMLR, 2021, pp. 1918–1929

2021
[12]

Rajamani, Vehicle dynamics and control

R. Rajamani, Vehicle dynamics and control . Springer Science & Business Media, 2011

2011
[13]

Repetitive learning model predictive control: An autonomous racing example,

M. Brunner, U. Rosolia, J. Gonzales, and F. Borrelli, “Repetitive learning model predictive control: An autonomous racing example,” in 2017 IEEE 56th annual conference on decision and control (CDC), IEEE, 2017, pp. 2545–2550

2017
[14]

Optimization-based system identification and moving horizon estimation using low- cost sensors for a miniature car-like robot,

S. Bodmer, L. V ogel, S. Muntwiler, et al. , “Optimization-based system identification and moving horizon estimation using low- cost sensors for a miniature car-like robot,” arXiv preprint arXiv:2404.08362, 2024

2024 arXiv
[15]

Learning- based model predictive control for autonomous racing,

J. Kabzan, L. Hewing, A. Liniger, and M. N. Zeilinger, “Learning- based model predictive control for autonomous racing,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 3363–3370, 2019

2019
[16]

Learning-based on-track system identification for scaled autonomous racing in under a minute,

O. Dikici, E. Ghignone, C. Hu, et al. , “Learning-based on-track system identification for scaled autonomous racing in under a minute,” IEEE Robotics and Automation Letters , 2025

2025
[17]

Adaptive planning and control with time-varying tire models for autonomous racing using extreme learning machine,

D. Kalaria, Q. Lin, and J. M. Dolan, “Adaptive planning and control with time-varying tire models for autonomous racing using extreme learning machine,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , IEEE, 2024, pp. 10 443–10 449

2024
[18]

Adaptive control using multi- ple models,

K. S. Narendra and J. Balakrishnan, “Adaptive control using multi- ple models,” IEEE transactions on automatic control, vol. 42, no. 2, pp. 171–187, 1997

1997
[19]

P. S. Maybeck, Stochastic models, estimation, and control . Aca- demic press, 1982, vol. 3

1982
[20]

Learning how to autonomously race a car: A predictive control approach,

U. Rosolia and F. Borrelli, “Learning how to autonomously race a car: A predictive control approach,” IEEE Transactions on Control Systems Technology, vol. 28, no. 6, pp. 2713–2719, 2019

2019
[21]

Cautious nmpc with gaussian process dynamics for autonomous miniature race cars,

L. Hewing, A. Liniger, and M. N. Zeilinger, “Cautious nmpc with gaussian process dynamics for autonomous miniature race cars,” in 2018 European control conference (ECC) , 2018, pp. 1341–1348

2018
[22]

Tyre modelling for use in vehicle dynamics studies,

E. Bakker, L. Nyborg, and H. B. Pacejka, “Tyre modelling for use in vehicle dynamics studies,” SAE transactions, pp. 190–204, 1987

1987
[23]

Minimum curvature trajectory plan- ning and control for an autonomous race car,

A. Heilmeier, A. Wischnewski, L. Hermansdorfer, J. Betz, M. Lienkamp, and B. Lohmann, “Minimum curvature trajectory plan- ning and control for an autonomous race car,” Vehicle System Dynamics, 2020

2020
[24]

Carla: An open urban driving simulator,

A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V . Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning, PMLR, 2017, pp. 1–16

2017

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Autonomous vehicles on the edge: A survey on autonomous vehicle racing,

J. Betz, H. Zheng, A. Liniger, et al., “Autonomous vehicles on the edge: A survey on autonomous vehicle racing,” IEEE Open Journal of Intelligent Transportation Systems , vol. 3, pp. 458–488, 2022

work page 2022

[2] [2]

Optimization-based autonomous racing of 1: 43 scale rc cars,

A. Liniger, A. Domahidi, and M. Morari, “Optimization-based autonomous racing of 1: 43 scale rc cars,” Optimal Control Ap- plications and Methods , vol. 36, no. 5, pp. 628–647, 2015

work page 2015

[3] [3]

Model parameter identifi- cation via a hyperparameter optimization scheme for autonomous racing systems,

H. Seong, C. Chung, and D. H. Shim, “Model parameter identifi- cation via a hyperparameter optimization scheme for autonomous racing systems,” IEEE Control Systems Letters , vol. 7, pp. 1652– 1657, 2023

work page 2023

[4] [4]

Motion planning and control for multi vehicle autonomous racing at high speeds,

A. Raji, A. Liniger, A. Giove, et al., “Motion planning and control for multi vehicle autonomous racing at high speeds,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2022, pp. 2775–2782

work page 2022

[5] [5]

Analysis and control of high sideslip manoeuvres,

C. V oser, R. Y . Hindiyeh, and J. C. Gerdes, “Analysis and control of high sideslip manoeuvres,” Vehicle System Dynamics , vol. 48, no. S1, pp. 317–336, 2010

2010

[6] [6]

Model-and acceleration-based pursuit controller for high-performance autonomous racing,

J. Becker, N. Imholz, L. Schwarzenbach, E. Ghignone, N. Baumann, and M. Magno, “Model-and acceleration-based pursuit controller for high-performance autonomous racing,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) , IEEE, 2023, pp. 5276–5283

work page 2023

[7] [7]

Online adaptation of learned vehicle dynamics model with meta-learning approach,

Y . Tsuchiya, T. Balch, P. Drews, and G. Rosman, “Online adaptation of learned vehicle dynamics model with meta-learning approach,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , IEEE, 2024, pp. 802–809

work page 2024

[8] [8]

Agile mobility with rapid online adaptation via meta-learning and uncertainty-aware mppi,

D. Kalaria, H. Xue, W. Xiao, T. Tao, G. Shi, and J. M. Dolan, “Agile mobility with rapid online adaptation via meta-learning and uncertainty-aware mppi,” in 2025 International Conference on Robotics and Automation (ICRA) , 2025

work page 2025