  Autonomous driving and advanced active safety features require accurate
high-resolution sensing capabilities. Automotive radars are the key component
of the vehicle sensing suit. However, when these radars operate in proximity to
flat surfaces, such as roads and guardrails, they experience a multipath
phenomenon that can degrade the accuracy of the direction-of-arrival (DOA)
estimation. Presence of multipath leads to misspecification in the radar data
model, resulting in estimation performance degradation, which cannot be
reliably predicted by conventional performance bounds. In this paper, the
misspecified Cram\'er-Rao bound (MCRB), which accounts for model
misspecification, is derived for the problem of DOA estimation in the presence
of multipath which is ignored by the estimator. Analytical relations between
the MCRB and the Cram\'er-Rao bound are established, and the DOA estimation
performance degradation due to multipath is investigated. The results show that
the MCRB reliably predicts the asymptotic performance of the misspecified
maximum-likelihood estimator and therefore, can serve as an efficient tool for
automotive radar performance evaluation and system design.
