ISI Report

The evolution of autonomous mobility systems has made real-time inference a cornerstone for ensuring safety and reliability in software-defined vehicles (SDVs). As mobility environments grow increasingly dynamic, the demand for low-latency, high-accuracy inference mechanisms become paramount. This study examines the optimization of real-time inference in SDVs, focusing on the interplay of deep learning algorithms, GPU acceleration, latency reduction, and safety-critical applications. By integrating advanced methods of latency optimization, GPU resource allocation, and adaptive scheduling frameworks, the research underscores strategies to achieve near-deterministic inference times in rapidly changing environments. The findings highlight how optimization techniques can significantly reduce computational overhead while preserving the accuracy required for safety-driven decision-making in SDVs. Central to this work is the recognition that inference latency directly influences the capacity of vehicles to avoid hazards, maintain mobility safety, and adapt to uncertain road conditions. Prior studies have demonstrated that improvements in parallelization, memory efficiency, and dynamic workload balancing can markedly enhance inference throughput without compromising stability. Furthermore, real-time monitoring of inference pipelines coupled with adaptive reconfiguration ensures resilience in unexpected operational scenarios. The methodology applied here incorporates system-level simulation, real-world data processing, and hardware-aware optimization strategies designed to support consistent safety outcomes. The results of this research provide evidence that SDVs can achieve a balance between computational efficiency and stringent safety requirements through integrated frameworks that harmonize inference speed, hardware acceleration, and intelligent scheduling. Ultimately, this paper emphasizes the essential role of real- time inference optimization in advancing mobility safety, offering a roadmap for future developments in intelligent mobility systems that rely on the synergy of software-defined architectures, deep learning, and robust real-time computation.