Automotive edge systems face a growing gap between computational demand and what vehicle platforms can supply under tight power and thermal budgets, especially in autonomous vehicles. Neuromorphic computing is proposed as response, owing to its event driven operation. But earlier reviews on this subject tend to mix demonstrated uses with speculative applications and do not always relate efficiency claims to real driving conditions. This review addresses this gap in the literature; automotive system integration of neuromorphic hardware, spiking neural network training and deployment, event based sensing. Reviewed studies are separated into demonstrated implementations with measurable outcomes on stated platform and proposed opportunities that still lack automotive grade validation. Four observations are obtained from this review. First, efficiency gains from spike based processing become credible mainly when the workload is sparse and temporal by nature and when coding policy is selected with bounded time to decision in mind. Second, cross study comparison remains difficult because latency, energy, event rate condition, and stopping rule are usually reported in inconsistent ways across published studies. Third, deployment barriers are largely procedural, including toolchain maturity, integration of asynchronous accelerators with synchronous ECU timing, and the construction of safety arguments under ISO 26262 and SOTIF. Fourth, public industrial activity is still concentrated on bounded functions such as driver monitoring, keyword spotting, and radar pre processing rather than full neuromorphic autonomy stacks. Based on these findings, a deployment roadmap is proposed around always on modules with explicit timing contracts, automotive grade benchmark suites, and safety case patterns that constrain learning and enforce monitorable behavioral contracts.
I. Can Dikmen (2026) studied this question.