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This research addresses a critical gap in autonomous driving safety by validating AI-generated radar interpretations against fundamental physics principles, ensuring that object estimates remain physically consistent even in unseen scenarios. By integrating a post-processing consistency module based on radar signature modeling, the approach corrects implausible hypotheses without requiring neural network retraining or architectural changes. This method significantly enhances trajectory prediction and collision assessment reliability for pre-crash safety systems.
Physics-Based Plausibility Assessment of AI-Predicted Vehicle Parameters for Automotive Radar in Pre-Crash Scenarios
arXiv:2611.04871v1 Announce Type: new Abstract: Automotive radar is a key sensing modality for safety-critical vehicle functions due to its robustness under adverse weather and lighting conditions. Recent advances in AI-based radar perception enable the direct estimation of object parameters from a single radar measurement cycle. However, the internal decision process of data-driven models is typically not explicitly linked to the underlying radar sensing physics. Consequently, object estimates may achieve high statistical accuracy while remaining physically inconsistent with the measured radar signature, particularly in previously unseen scenarios. Such inconsistencies are especially critical for heading estimation, as they directly affect trajectory prediction and collision assessment in pre-crash scenarios. This paper presents a physics-based consistency assessment framework for validating AI-derived vehicle hypotheses in automotive radar perception. The proposed approach generates an expected radar signature from AI-estimated object parameters using a physics-based reference model that incorporates occlusion-dependent edge visibility, aspect- and angle-dependent RCS modeling, and characteristic wheel-induced MD signatures. The expected signature is compared with the observed radar point cloud using a Wasserstein-distance-based consistency metric computed from three complementary features: the spatial distribution of detections along object edges, the local detection count, and the radar reflection intensity. In this way, physically implausible object hypotheses can be identified and corrected independently of the underlying neural network architecture and without model retraining or architectural modifications. The proposed consistency module is integrated as a cascaded post-processing stage for vehicle heading estimation.
Abstract:Automotive radar is a key sensing modality for safety-critical vehicle functions due to its robustness under adverse weather and lighting conditions. Recent advances in AI-based radar perception enable the direct estimation of object parameters from a single radar measurement cycle. However, the internal decision process of data-driven models is typically not explicitly linked to the underlying radar sensing physics. Consequently, object estimates may achieve high statistical accuracy while remaining physically inconsistent with the measured radar signature, particularly in previously unseen scenarios. Such inconsistencies are especially critical for heading estimation, as they directly affect trajectory prediction and collision assessment in pre-crash scenarios. This paper presents a physics-based consistency assessment framework for validating AI-derived vehicle hypotheses in automotive radar perception. The proposed approach generates an expected radar signature from AI-estimated object parameters using a physics-based reference model that incorporates occlusion-dependent edge visibility, aspect- and angle-dependent RCS modeling, and characteristic wheel-induced MD signatures. The expected signature is compared with the observed radar point cloud using a Wasserstein-distance-based consistency metric computed from three complementary features: the spatial distribution of detections along object edges, the local detection count, and the radar reflection intensity. In this way, physically implausible object hypotheses can be identified and corrected independently of the underlying neural network architecture and without model retraining or architectural modifications. The proposed consistency module is integrated as a cascaded post-processing stage for vehicle heading estimation.
Robotics (cs.RO)
arXiv:2611.04871 [cs.RO]
(or arXiv:2611.04871v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.04871 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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