# Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis
## 5 Role of Hardware in Agent-Based Simulation  
A major drawback of ABMs is the long simulation time for large models. ABM has seen increasing interest in leveraging hardware accelerators to enhance computational efficiency and scalability. To support computationally intensive ABM simulations, hardware accelerators such as GPUs, FPGAs, and ASICs offer significant advantages. Table 7 summarizes their relative strengths and weaknesses across key dimensions such as performance, cost, and scalability. For this study, the use of GPUs was considered due to their availability and suitability for high-parallelism tasks. GPUs enable the rapid execution of floating-point operations, which are critical for simulating agent dynamics and interactions.  
These accelerators, including GPUs, FPGAs, and custom ASICs, offer specialized architectures that can significantly speed up simulations by parallelizing and offloading computational tasks. Recent research continues to explore GPUs as powerful accelerators for ABM. GPUs provide massively parallel architectures with high memory bandwidth, making them suitable for handling large-scale simulations with complex agent interactions. Studies have shown significant speed-ups compared to CPU implementations, enabling simulations in epidemiology, urban planning, and ecological modelling to achieve faster execution times and higher scalability [45; 46]. FPGAs offer reconfigurable hardware that can be customized to optimize specific ABM algorithms and agent interaction rules. Re  
Figure 17: Estimated vehicle trajectory vs. ground truth trajectory before using vehicle.  
Figure 18: Vehicle’s IMU data after driving.  
cent advancements focus on designing FPGA-based accelerators that balance throughput, latency, and power efficiency. For instance, FPGA implementations have been successful in real-time decision-making applications and sensor network simulations, showcasing their flexibility and performance advantages [47; 48]. Custom ASICs provide specialized solutions for ABM tasks requiring high performance and energy efficiency. Recent developments include domain-specific designs tailored for genetic algorithms and biochemical reaction networks within ABM frameworks. These ASICs demonstrate superior performance metrics such as low power consumption and high throughput, addressing specific computational challenges in complex agent-based simulations [49]. The recent literature highlights ongoing efforts to optimize hardware accelerators for ABM, emphasizing performance improvements, energy efficiency, and application-specific customization. Challenges remain in programming models, hardware integration, and scalability across different ABM domains. Future research directions may explore hybrid accelerator architectures, advanced simulation algorithms, and integration with emerging technologies such as AI and edge computing. Consideration must also be given to how this improved hardware is implemented. Agent-based simulations can be accelerated by making efficient use of hardware though optimisation of algorithms, for example, the trade-off between precision and speed. Execution times can be reduced by allowing small errors in results, which can be achieved by using approximate algorithms. Reducing the number of executed operations is a simple way to improve execution times. Without optimisation in computer memory, improvements in simulation speed will still be limited by memory layout, even when simulation code has been optimised through the parallelisation of operations. Independence from hardware is also advisable, with approaches such as unified memory access that abstract from the hardware specifics negating the need for model developers to become experts in working with particular hardware accelerators.