Intelligent vehicles (IVs) need a perception system to model the surrounding environment. The Hybrid Sampling Bayesian Occupancy Filter (HSBOF) is a perception algorithm monitoring a grid-based model of the environment called " occupancy grid ". It is a highly data-parallel algorithm and requires a high computational performance to be executed in reasonable time. It is currently implemented in CUDA on a NVIDIA GPU. However, the GPU is power consuming and its purchase cost is too high for the embedded market. In this paper, we prove that, the couple embedded many-core/OpenCL is a feasible hardware/software architecture for replacing the GPU/CUDA. Our OpenCL implementation and experimental results on a testing hardware showed that a many-core can produce an occupancy grid every 168ms while consuming 40 times less power than the GPU. The results are promising for a future integration into IVs.