For self-driving cars, real-time decision-making is crucial. They must handle huge amounts of sensor data that streams in and is available all while responding to a streetscape that changes by the second. Traditional sequential processing models are inherently reliable but can be unbelievably slow during special moments when an immediate reaction is required. This paper explores how parallel processing delivers superior performance to the autonomous driving system. We present the design and simulation-based analysis of real-time algorithms for object recognition, sensor fusion and decision-making in both sequential and parallel fashion. This is implemented with multi-core CPU and GPU architectures. In a simulated environment, our parallel approach for object detection combines the advantages of handling multiple sensor data with concurrent development based on convolutional neural networks. Not only is the system latency dramatically reduced, but it also allows us to offload from the work we have done on a previous issue. Our research is based on conceptual modelling and software-based simulations to investigate the performance benefits of parallel processing. The findings show improved response times and greater robustness under environmental variation. We also touch on the challenges of balancing loads and their methods to accommodate the distributed system in real-time constraints, while still keeping a secure and reliable environment. These findings underscore the need for parallel processing to make the responsiveness and reliability requirements of autonomous vehicle systems possible. This paper provides a theoretical and computational foundation upon which future real-time decision-making for self-driving technology can be built.