aiqbench analysis
This research evaluates the trade-offs between model complexity and positional accuracy for single-track vehicle models used in autonomous driving motion planning. By systematically comparing three distinct models against real-world measurements, it provides critical insights for optimizing Model Predictive Control applications without relying on a one-size-fits-all approach. The findings are essential for engineers balancing computational efficiency with trajectory safety requirements in robotics systems.
Short-Horizon Position Accuracy of Single-Track Models: Implications for Motion Planning of Autonomous Vehicles
arXiv:2606.14216v1 Announce Type: new Abstract: Accurate and computationally efficient vehicle models are essential for motion planning of autonomous vehicles, where positional accuracy directly affects trajectory feasibility and safety. However, the positional accuracy has not been systematically evaluated against real measurements. Therefore, this paper compares the short-horizon positional accuracy of three single-track vehicle models against vehicle measurements across various driving maneuvers. Model parameters are identified through dedicated experiments with the instrumented test vehicle. Rather than identifying a single best model, this work aims to provide insight into the trade-offs between model complexity, parameterization quality, and positional accuracy for informed model selection in Model Predictive Control applications.
Abstract:Accurate and computationally efficient vehicle models are essential for motion planning of autonomous vehicles, where positional accuracy directly affects trajectory feasibility and safety. However, the positional accuracy has not been systematically evaluated against real measurements. Therefore, this paper compares the short-horizon positional accuracy of three single-track vehicle models against vehicle measurements across various driving maneuvers. Model parameters are identified through dedicated experiments with the instrumented test vehicle. Rather than identifying a single best model, this work aims to provide insight into the trade-offs between model complexity, parameterization quality, and positional accuracy for informed model selection in Model Predictive Control applications.
Submitted to The International Journal of Automotive Engineering, Official Journal of the Society of Automotive Engineers of Japan, Inc. (JSAE)
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2606.14216 [cs.RO]
(or arXiv:2606.14216v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.14216 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 217 words