In this paper, a new integrity risk evaluation method is developed and tested for laser and radar-based navigation algorithms using feature extraction (FE) and data association (DA). This work is intended for safety-critical autonomous vehicle navigation. FE and DA are two pre-estimator measurement processing steps that aim at repeatedly and consistently identifying landmarks in the environment. A major risk for safety in FE and DA is caused by incorrect associations (mistaking one landmark for another). To assess this risk, a criterion is first introduced at FE: it establishes the minimum normalized separation between landmarks ensuring that they can be reliably, quantifiably distinguished. Then, an innovation-based DA process is designed, which provides the means to evaluate the probability of incorrect associations while considering all potential measurement permutations. These algorithms are analyzed and tested, showing the impact of incorrect associations on safety risk.