Multi-Modal Model Predictive Path Integral Control for Collision Avoidance

Alberto Bertipaglia; Barys Shyrokau; Dariu M. Gavrila

REVIEW 5 major objections 5 minor 16 references

Multi-Modal Model Predictive Path Integral Control for Collision Avoidance

T0 review · 5 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A multi-modal model predictive path integral controller sampling around four action modes—prior input, hard braking, hard acceleration, and a wary evasive maneuver—avoids collisions where the standard single-mode MPPI fails.

desk verdict A coherent combination of Sobol sampling and hand-crafted modes for MPPI collision avoidance, with a promising demo but an uncontrolled baseline that leaves the core claim unproven. read the letter →

arxiv 2508.21364 v1 pith:7C7PW2TM submitted 2025-08-29 cs.RO cs.SYeess.SY

Alberto Bertipaglia , Dariu M. Gavrila , Barys Shyrokau This is my paper · ORCID

classification cs.ROcs.SYeess.SY

keywords modelpredictivepathintegralcontrolmulti-modalsamplingSobolsequencescollisionavoidanceautonomousvehiclesFialatirefrictioncirclemotionplanning

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

This paper argues that a sampling-based motion planner for automated vehicles can escape local minima by sampling its control sequences around several distinct 'modes' at once, rather than around the single previous control input. The proposed controller is built on model predictive path integral (MPPI) control—a sampling-based stochastic optimal control algorithm that forward-simulates many input sequences and weights them by trajectory cost—and draws its rollouts from Sobol sequences (low-discrepancy quasi-random point sets) centered on four means: the previously optimized input, maximum braking, maximum acceleration, and an evasive maneuver adapted from the 'wary approach.' In high-fidelity simulation, the multi-modal controller completes double lane changes on high- and low-friction roads and an occlusion scenario with two crossing obstacles, while a standard MPPI baseline collides or leaves the road in the tighter versions of each test. The practical stakes: a single planner can decide between stopping, going around, and going faster without a separate high-level decision layer, and can execute that choice at the limits of tire adhesion.

What carries the argument

The core element is the multi-modal sampler: four means for Sobol-sequence rollouts, active below a 2 s time-to-closest-approach. The first mean is the standard MPPI prior input; the other three are analytical solutions—maximum braking, maximum acceleration, and a wary evasive maneuver. Sobol sequences cover the control space more uniformly than Gaussian sampling over long horizons. Each mode carries tailored cost terms (braking accepts velocity error; evasion enforces obstacle and road-edge margins); per-mode importance weights merge into one control sequence. The prediction model is a nonlinear single-track vehicle with Fiala tires (a tire model capturing force saturation) and a friction-c

What would settle it

A concrete test: in the same high-fidelity simulator, place an obstacle so the only collision-free action is intermediate—a constant 3 m/s² deceleration followed by a gentle lane change—sitting between the maximum-braking and wary-evasion modes and far from the prior input. Vary initial speed and obstacle offset across many runs and record whether the multi-modal controller stays collision-free. The claim predicts failure whenever the optimal sequence lies in a cost basin covered by none of the four sampling distributions, so the controller should degrade to baseline-level collisions in this i

Watch

Extended reading notes

Core claim

Multi-modal sampling restores feasibility to MPPI in time-critical emergencies. Standard MPPI concentrates rollouts near the previously optimized input, so when the required action changes qualitatively—brake instead of steer, accelerate instead of slow—it stays trapped in a local minimum. The multi-modal version adds three sampling modes when the time to closest approach drops below two seconds: maximum braking, maximum acceleration, and a wary evasive maneuver, each with tailored cost terms. In simulation it completes double lane changes at 2 s and 1.7 s time-to-collision, a low-friction double lane change, and an occluded crossing-obstacle scenario; the standard baseline fails in the tigh

Load-bearing premise

The load-bearing premise is that one of the four sampling modes—prior input, maximum braking, maximum acceleration, or the wary evasive maneuver—lies close to a feasible avoidance action in every scenario the vehicle encounters, because the planner only explores perturbations around these means and cannot discover an optimal action far from all of them.

Editorial extensions

If this is right

A single planner can arbitrate between qualitatively different avoidance strategies—braking, steering around, accelerating through—without a separate behavioral layer, with mode weights making the transition continuous.
Because rollouts are evaluated with a nonlinear single-track model and friction-circle constraints, the planner operates up to the handling limits on low-friction roads, a regime where linear-model planners are not valid.
The approach is real-time in the paper's setup: 2,600 parallel rollouts solved in 25.4 ms on average (31.6 ms maximum) on a GPU.
The mode set is a design choice, giving a direct way to inject domain knowledge—'these are the sensible things to try'—into sampling-based MPC.
The demonstrated behavior is so far simulation-based; the stated next step is validation on a real vehicle at a test track.

Reading between the lines

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

Editorial extension: if the central claim is right, the mode set is the planner's hypothesis space—an encounter is avoidable only when the correct action lies near one of the four sampling means, so intermediate maneuvers (e.g., sustained moderate braking) are the predicted failure mode and can be tested by varying obstacle timing.
Editorial extension: the two-second activation threshold is a discrete switch; continuous blending of mode weights or learned mode priors could smooth strategy transitions and remove a tuning parameter.
Editorial extension: the design principle—pair each sampling mean with a cost function that keeps its strategy visible to the optimizer—transfers to other sampling-based planners, such as drone or legged-robot navigation, whenever sensible actions decompose into a few prototypes.

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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

Core thing to know: this is a real, workmanlike engineering paper with a clean algorithmic statement, and the simulations do back up the qualitative story they tell. The genuinely new bit is the specific package: Sobol low-discrepancy sampling around four hand-defined modes (previous input, maximum braking, maximum acceleration, wary evasive) inside an MPPI loop, paired with a nonlinear single-track Fiala tyre model and friction-circle constraints. Multi-modal MPPI and Sobol sampling already exist separately (refs [16] and [11,15]), so the "first multi-modal MPPI" claim is too strong and should be softened to a combination claim. The paper does that combination carefully: the equations are coherent, the cost function is laid out in detail, and the real-time numbers (25.4 ms average solve time) are plausible. The occlusion scenario where acceleration is the only way out is a nice demonstration that the modes genuinely cover distinct strategies.

The soft spots are real but not fatal. The big one: the comparison to the "standard MPPI" baseline is uncontrolled. The paper never specifies the baseline's sampling distribution, sample count, variance, cost function, or whether it used the same vehicle model and cost weights. The proposed method differs from the baseline in two independent ways — Sobol sampling and the extra modes — so the observed wins in Figs. 2–4 could in principle come entirely from better low-discrepancy coverage. Without an ablation that runs single-mode Sobol against Gaussian, or better, a comparison to Biased-MPPI [12] and the existing Multi-Modal MPPI [16], the central claim is not nailed down. That is the difference between a solid demo and a solid contribution, and it is fixable.

Second soft spot: every scenario is a single deterministic run, no repeated seeds, no statistical spread. For a sampling-based controller that is a real omission. Also, the tuned cost weights and tyre parameters are not reported; the paper says they were optimized but gives no values, which blocks independent replication. Minor: the hand-picked modes mean the method will only help when the right escape action is near one of the four anchors; the paper does not discuss when that assumption fails.

Overall: this deserves a serious referee, because the method is coherent and the problem is relevant, but the authors need to provide a proper baseline, an ablation, and more than one run per scenario before I would believe the headline claim. I would not cite it in my own work in the next year, but I would keep it on the list as a related-work pointer once the evaluation is cleaned up.

Referee Report

5 major / 5 minor

Summary. The paper proposes a multi-modal Model Predictive Path Integral (MPPI) controller for automated vehicle collision avoidance. The controller samples control sequences with Sobol low-discrepancy sequences around four modes: the previously optimized input, maximum braking, maximum acceleration, and an evasive maneuver based on a 'wary' strategy. The prediction model is a nonlinear single-track vehicle model with a Fiala tire model and friction-circle constraints. The authors evaluate the approach in high-fidelity simulations (IPG CarMaker) in double lane change scenarios under high- and low-friction conditions and in an occlusion scenario with crossing obstacles, comparing against a 'standard MPPI' baseline [11]. The claimed contributions are a first multi-modal MPPI with Sobol sampling, and a planner able to operate at the handling limits using nonlinear tire dynamics.

Significance. If the central claim is supported, the paper would make a useful contribution: demonstrating that sampling around multiple analytical strategies can rescue MPPI from local minima in critical collision-avoidance situations, while retaining real-time feasibility (average 25.4 ms per update). The use of a Fiala tire model with friction-circle constraints is a sensible step toward limit-handling motion planning. The Sobol-sequence sampling is a reasonable, low-cost improvement for trajectory diversity. However, the current evidence is not sufficient to establish the claimed advantage: the baseline is not specified in enough detail, no ablation isolates the multi-modal component, and the results are single deterministic runs with no quantitative uncertainty analysis. The novelty claim is also weakened by the existence of prior 'Multi-modal MPPI' work (Ref. [16]).

major comments (5)

[Section 4 and Experimental Setup] The comparison against the 'standard MPPI' baseline [11] is not controlled. The paper does not state whether the baseline uses the same cost function (Eq. 4), the same vehicle model (Eq. 3), the same Sobol sampling, the same number of samples, the same horizon, or the same cost weights. The proposed method differs from the baseline in at least two independent aspects: Sobol sampling and the added modes. Without ablations (e.g., single-mode Sobol, standard sampling with modes, and the full method), the observed improvements in Figs. 1-4 cannot be attributed to multi-modal sampling. This is load-bearing for the central claim and must be addressed with more detailed baseline description and ablation experiments.
[Section 2.4] The mechanism for combining mode-specific importance weights is described only verbally: 'Each mode computes its own set of importance weights, which are combined to produce a final control sequence.' No formula, normalization, or blending rule is provided. This is necessary for reproducibility and for assessing whether mode transitions can cause discontinuities or instability. Please specify the combination rule and justify it, for example with respect to Ref. [16].
[Contributions and Section 1] The paper claims to introduce 'the first multi-modal MPPI' (Contributions, Section 1), yet Ref. [16] is titled 'Multi-modal MPPI and Active Inference for Reactive Task and Motion Planning' and is cited as related work. Either the novelty claim is false or the difference from [16] must be articulated precisely (e.g., the use of Sobol sequences, the specific auxiliary modes, or the vehicle-dynamics focus). As written, the claim is internally inconsistent with the cited literature.
[Sections 4.1-4.3] Each scenario is a single deterministic run without statistical analysis or quantitative metrics. The paper reports qualitative trajectory plots and velocity profiles, but no numerical results such as minimum distance to obstacles, maximum sideslip, road-exit margin, or success rates across multiple initial conditions or noise realizations. Given that the paper's conclusion is that the proposed method 'outperforms' standard MPPI, the absence of repeated trials and quantitative comparison is a major gap. Please provide statistics over multiple runs and report the chosen random seeds or noise settings.
[Sections 2.2-2.3 and 3] The cost weights q_* in Eq. (4), the Fiala tire parameters, the friction-circle safety factor Sc, and the MPPI temperature λ are described as 'optimised' or 'tuned' but their values are not reported. This prevents reproducibility and makes it impossible to assess whether the reported advantage depends on fine-tuning that favors the proposed method. The baseline's tuning is also unreported. Please include the parameter values and, ideally, a sensitivity analysis with respect to the most important weights.

minor comments (5)

[Section 2.2] The state vector is written as [X, Y, phi, vx, vy, r, theta, delta, ax], but Eq. (3) uses ψ for the heading angle and 'r' appears both as yaw rate and as part of the state vector with a different meaning (road-wheel angle rate). Please clarify notation and use consistent symbols.
[Section 2.3] In Eq. (4), the notation qeV el and eV el is awkward; the text later refers to qV2O while the formula uses qeV2O. The safe distances D_Sf t,O and D_Sf t,E are not defined explicitly. Also, the Log-Cosh velocity penalty in Eq. (5) is not labeled as a penalty weight q_vel. Please clean up the notation.
[Section 3] The paper states the solver achieves 25.4 ms average and 31.6 ms maximum, but also says 'mathematical guarantees for convergence within real-time constraints are not provided.' Since the control period is 50 ms, the nominal timing is fine, but please state whether the 31.6 ms maximum includes all runs and whether any sample was dropped to meet the real-time deadline.
[Section 2.4] The activation condition for the auxiliary modes is 'TCPA falls below 2 s.' No definition of TCPA is given, nor is the choice of the 2 s threshold justified. Please provide the formula and a brief explanation of the threshold selection.
[References] The introduction cites Refs. [13,16] for claims about Sobol and Halton sequences; some of these citations appear to be about MPPI variants rather than low-discrepancy sequences. Please verify that the citations support the specific statements.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; the multi-modal MPPI pipeline is self-contained, with only mild reliance on the authors' prior component models.

full rationale

The paper's central claim is an empirical comparison between a multi-modal MPPI and a standard MPPI baseline, not a derivation of a first-principles result. The MPPI importance-weighting formula (Eq. 1) is the standard formulation, and the four sampling modes (prior input, max braking, max acceleration, wary evasive maneuver) are externally motivated inputs adopted from prior work ([4] for the wary approach; [1,3] for the vehicle model and cost structure). The success in the DLC and occlusion scenarios is not a fitted prediction: the controller outputs are produced by forward simulation and cost-weighted aggregation, and no parameter is fit to the success metric. The auxiliary modes are hand-selected to cover plausible avoidance strategies, which makes the reported successes somewhat unsurprising, but that is a limitation of the experimental design (no ablation or comparison to Biased-MPPI / prior Multi-Modal MPPI), not circularity. The paper's use of self-citations for the model and cost terms is real prior work and does not constitute a self-citation chain that forces the conclusion. Therefore no circular step meeting the quotation/reduction bar is present.

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

The central algorithm rests on standard MPPI theory, a vehicle model, and three auxiliary analytical strategies. The paper contributes a sampling scheme that combines these; it does not introduce new physical entities. The main burden comes from numerous tuned parameters and unstated hyperparameters, and from trusting the prediction model and mode set to cover the encountered scenarios.

free parameters (6)

Cost function weights q_*
Section 2.3, eq. (4) lists q_eCon, q_eLag, q_eVel, q_dδdot, q_daxdot, qδ, qax, qβ, qr, qTf, qSt, qV2O, qV2E; text says 'These are optimised to minimise longitudinal velocity errors and reduce sideslip peaks [3].' Values are not reported.
Fiala tyre parameters
Section 2.2: 'The tyre parameters are optimised through quasi-steady-state circular driving in a high-fidelity simulation based on a Delft-Tyre model 6.1.' Values not reported.
Safety factor Sc = 0.95
Section 2.3: 'Due to estimation uncertainties, a safety factor Sc of 0.95 is applied to reduce the maximum allowable longitudinal force.'
MPPI temperature parameter λ
Section 2.1 defines λ as the weighting temperature but gives no value; it strongly influences exploitation/exploration.
TCPA activation threshold = 2 s
Section 2.4: auxiliary modes are activated when TCPA falls below 2 s; this is a hand-chosen threshold.
Sampling variance/covariance and mode sample allocation
Section 2.4 does not specify the distribution width around each mode mean or how the total 2600 samples are split across the four modes.

assumptions (5)

standard math Importance-weight exponential cost weighting (Eq. 1) approximates the optimal control solution in the MPPI framework.
Taken from Williams et al. [13,14]; the paper relies on it without derivation in Section 2.1.
domain assumption The nonlinear single-track vehicle model with Fiala tyre and friction-circle coupling adequately predicts vehicle motion at the limits of handling.
Section 2.2; the controller's success depends on this model being a faithful predictor.
domain assumption The four sampling modes (prior, maximum braking, maximum acceleration, wary evasive maneuver) span the relevant avoidance strategies for the tested scenarios.
Section 2.4; if a feasible avoidance strategy is not captured by these modes, the planner has no way to discover it through sampling around them.
domain assumption Sobol low-discrepancy sequences provide better or equal exploration than Gaussian sampling in this MPPI implementation.
Section 2.1; asserted with citations [11,15], no proof specific to this cost/dynamics.
domain assumption The tuned cost function weights transfer from the optimization dataset to the reported test scenarios.
Section 2.3; weights are optimized per [3], and their generalization to double lane change and occlusion scenarios is assumed.

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Cite this review

Pith. "Pith review of Multi-Modal Model Predictive Path Integral Control for Collision Avoidance." pith.science (2026). https://pith.science/paper/7C7PW2TM

@misc{pith2026250821364,
  author       = {Pith},
  title        = {Pith review of: Multi-Modal Model Predictive Path Integral Control for Collision Avoidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7C7PW2TM}},
  note         = {Machine review of arXiv:2508.21364}
}

read the original abstract

This paper proposes a novel approach to motion planning and decision-making for automated vehicles, using a multi-modal Model Predictive Path Integral control algorithm. The method samples with Sobol sequences around the prior input and incorporates analytical solutions for collision avoidance. By leveraging multiple modes, the multi-modal control algorithm explores diverse trajectories, such as manoeuvring around obstacles or stopping safely before them, mitigating the risk of sub-optimal solutions. A non-linear single-track vehicle model with a Fiala tyre serves as the prediction model, and tyre force constraints within the friction circle are enforced to ensure vehicle stability during evasive manoeuvres. The optimised steering angle and longitudinal acceleration are computed to generate a collision-free trajectory and to control the vehicle. In a high-fidelity simulation environment, we demonstrate that the proposed algorithm can successfully avoid obstacles, keeping the vehicle stable while driving a double lane change manoeuvre on high and low-friction road surfaces and occlusion scenarios with moving obstacles, outperforming a standard Model Predictive Path Integral approach.

Figures

Figures reproduced from arXiv: 2508.21364 by the authors.

**Figure 2.** Double lane change under high-friction conditions with TTC = 1 [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Obstacle occlusion scenario under high-friction conditions. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Double lane change under low-friction conditions. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

16 extracted references · 15 canonical work pages

[16]

IEEE Robotics and Automation Letters 9(9), 7461–7468 (2024)

Zhang, Y., Pezzato, C., Trevisan, E., Salmi, C., Corbato, C.H., Alonso-Mora, J.: Multi-modal mppi and active inference for reactive task and motion planning. IEEE Robotics and Automation Letters 9(9), 7461–7468 (2024)

work page 2024
[12]

IEEE Robotics and Automation Letters 9(6), 5871–5878 (2024)

Trevisan, E., Alonso-Mora, J.: Biased-mppi: Informing sampling-based model pre- dictive control by fusing ancillary controllers. IEEE Robotics and Automation Letters 9(6), 5871–5878 (2024)

work page 2024
[11]

In: IEEE International Conference on Robotics, Automation and Artificial Intelligence

Testouri, M., Elghazaly, G., Frank, R.: Towards a safe real-time motion planning framework for autonomous driving systems: A model predictive path integral ap- proach. In: IEEE International Conference on Robotics, Automation and Artificial Intelligence. pp. 231–238 (2023)

work page 2023
[1]

In: Symposium on the Dynamics of Vehicle on Roads and on Tracks (2023)

Bertipaglia, A., Alirezaei, M., Happee, R., Shyrokau, B.: Model predictive contour- ing control for vehicle obstacle avoidance at the limit of handling. In: Symposium on the Dynamics of Vehicle on Roads and on Tracks (2023)

work page 2023
[2]

IEEE Transac- tions on Vehicular Technology 73(9), 12731–12746 (2024)

Bertipaglia, A., Alirezaei, M., Happee, R., Shyrokau, B.: An unscented kalman filter-informed neural network for vehicle sideslip angle estimation. IEEE Transac- tions on Vehicular Technology 73(9), 12731–12746 (2024)

work page 2024
[3]

IEEE Transactions on Vehicular Technology 74(6), 8756–8771 (2025)

Bertipaglia, A., Tavernini, D., Montanaro, U., Alirezaei, M., Happee, R., Sorniotti, A., Shyrokau, B.: On the benefits of torque vectoring for automated collision avoid- ance at the limits of handling. IEEE Transactions on Vehicular Technology 74(6), 8756–8771 (2025)

work page 2025
[4]

IEEE Transactions on Intelligent Vehicles 6(2), 353–365 (2020)

Fors, V., Olofsson, B., Nielsen, L.: Autonomous wary collision avoidance. IEEE Transactions on Intelligent Vehicles 6(2), 353–365 (2020)

work page 2020
[5]

In: IEEE Intelligent Vehicles Symposium (2025)

de Groot, O., Bertipaglia, A., Boekema, H., Jain, V., Kegl, M., Kotian, V., Lentsch, T., Lin, Y., Messiou, C., Schippers, E., et al.: A vehicle system for navigating among vulnerable road users including remote operation. In: IEEE Intelligent Vehicles Symposium (2025)

work page 2025

Show all 16 references

[6]

IEEE Transactions on Robotics 41, 110–126 (2025)

de Groot, O., Ferranti, L., Gavrila, D.M., Alonso-Mora, J.: Topology-driven parallel trajectory optimization in dynamic environments. IEEE Transactions on Robotics 41, 110–126 (2025)

2025
[7]

IEEE Robotics and Automa- tion Letters 10(7), 7492–7499 (2025)

Mohamed, I.S., Ali, M., Liu, L.: Chance-constrained sampling-based mpc for col- lision avoidance in uncertain dynamic environments. IEEE Robotics and Automa- tion Letters 10(7), 7492–7499 (2025)

2025
[8]

IEEE Transactions on Robotics 41, 1172–1192 (2025)

Mohamed, I.S., Xu, J., Sukhatme, G.S., Liu, L.: Towards efficient mppi trajectory generation with unscented guidance: U-mppi control strategy. IEEE Transactions on Robotics 41, 1172–1192 (2025)

2025
[9]

arXiv preprint arXiv:2506.16386 (2025)

Park, L., Jang, K., Kim, S.: Csc-mppi: A novel constrained mppi framework with dbscan for reliable obstacle avoidance. arXiv preprint arXiv:2506.16386 (2025)

2025 arXiv
[10]

Advanced Robotics 14(6), 477–493 (2000)

Sim´ eon, T., Laumond, J.P., Nissoux, C.: Visibility-based probabilistic roadmaps for motion planning. Advanced Robotics 14(6), 477–493 (2000)

2000
[13]

Journal of Guidance, Control, and Dynamics 40(2), 344–357 (2017)

Williams, G., Aldrich, A., Theodorou, E.A.: Model predictive path integral control: From theory to parallel computation. Journal of Guidance, Control, and Dynamics 40(2), 344–357 (2017)

2017
[14]

In: IEEE International Con- ference on Robotics and Automation

Williams, G., Drews, P., Goldfain, B., Rehg, J.M., Theodorou, E.A.: Aggressive driving with model predictive path integral control. In: IEEE International Con- ference on Robotics and Automation. pp. 1433–1440 (2016)

2016
[15]

In: IEEE International Conference on Robotics and Automation

Yan, L.L., Devasia, S.: Output-sampled model predictive path integral control (o- mppi) for increased efficiency. In: IEEE International Conference on Robotics and Automation. pp. 14279–14285 (2024)

2024

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [16]

IEEE Robotics and Automation Letters 9(9), 7461–7468 (2024)

Zhang, Y., Pezzato, C., Trevisan, E., Salmi, C., Corbato, C.H., Alonso-Mora, J.: Multi-modal mppi and active inference for reactive task and motion planning. IEEE Robotics and Automation Letters 9(9), 7461–7468 (2024)

work page 2024

[2] [12]

IEEE Robotics and Automation Letters 9(6), 5871–5878 (2024)

Trevisan, E., Alonso-Mora, J.: Biased-mppi: Informing sampling-based model pre- dictive control by fusing ancillary controllers. IEEE Robotics and Automation Letters 9(6), 5871–5878 (2024)

work page 2024

[3] [11]

In: IEEE International Conference on Robotics, Automation and Artificial Intelligence

Testouri, M., Elghazaly, G., Frank, R.: Towards a safe real-time motion planning framework for autonomous driving systems: A model predictive path integral ap- proach. In: IEEE International Conference on Robotics, Automation and Artificial Intelligence. pp. 231–238 (2023)

work page 2023

[4] [1]

In: Symposium on the Dynamics of Vehicle on Roads and on Tracks (2023)

Bertipaglia, A., Alirezaei, M., Happee, R., Shyrokau, B.: Model predictive contour- ing control for vehicle obstacle avoidance at the limit of handling. In: Symposium on the Dynamics of Vehicle on Roads and on Tracks (2023)

work page 2023

[5] [2]

IEEE Transac- tions on Vehicular Technology 73(9), 12731–12746 (2024)

Bertipaglia, A., Alirezaei, M., Happee, R., Shyrokau, B.: An unscented kalman filter-informed neural network for vehicle sideslip angle estimation. IEEE Transac- tions on Vehicular Technology 73(9), 12731–12746 (2024)

work page 2024

[6] [3]

IEEE Transactions on Vehicular Technology 74(6), 8756–8771 (2025)

Bertipaglia, A., Tavernini, D., Montanaro, U., Alirezaei, M., Happee, R., Sorniotti, A., Shyrokau, B.: On the benefits of torque vectoring for automated collision avoid- ance at the limits of handling. IEEE Transactions on Vehicular Technology 74(6), 8756–8771 (2025)

work page 2025

[7] [4]

IEEE Transactions on Intelligent Vehicles 6(2), 353–365 (2020)

Fors, V., Olofsson, B., Nielsen, L.: Autonomous wary collision avoidance. IEEE Transactions on Intelligent Vehicles 6(2), 353–365 (2020)

work page 2020

[8] [5]

In: IEEE Intelligent Vehicles Symposium (2025)

de Groot, O., Bertipaglia, A., Boekema, H., Jain, V., Kegl, M., Kotian, V., Lentsch, T., Lin, Y., Messiou, C., Schippers, E., et al.: A vehicle system for navigating among vulnerable road users including remote operation. In: IEEE Intelligent Vehicles Symposium (2025)

work page 2025