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Autonomous driving and advanced active safety features require accuratehigh-resolution sensing capabilities. Automotive radars are the key componentof the vehicle sensing suit. However, when these radars operate in proximity toflat surfaces, such as roads and guardrails, they experience a multipathphenomenon that can degrade the accuracy of the direction-of-arrival (DOA)estimation. Presence of multipath leads to misspecification in the radar datamodel, resulting in estimation performance degradation, which cannot bereliably predicted by conventional performance bounds. In this paper, themisspecified Cram\'er-Rao bound (MCRB), which accounts for modelmisspecification, is derived for the problem of DOA estimation in the presenceof multipath which is ignored by the estimator. Analytical relations betweenthe MCRB and the Cram\'er-Rao bound are established, and the DOA estimationperformance degradation due to multipath is investigated. The results show thatthe MCRB reliably predicts the asymptotic performance of the misspecifiedmaximum-likelihood estimator and therefore, can serve as an efficient tool forautomotive radar performance evaluation and system design.

MCRB on DOA Estimation for Automotive MIMO Radar in the Presence of Multipath