Parallelization of particle filter based localization and map matching algorithms on multicore/manycore architectures

Parallelization is the key method to speedup advanced driving assistance systems (ADAS) and autonomous vehicle applications especially using emerging multicore and many core processors. Localization and map matching are among the fundamental parts of such applications. This paper presents a parallel implementation and performance analysis of particle filter based vehicle localization and map-matching algorithm both on a multicore processor using Open Multi-Processing (OpenMP) and on a graphics processing unit (GPU) using the Compute Unified Device Architecture (CUDA). In the proposed implementation, GPS and odometer data is fused with digital map information. Tests were performed on real data captured in vehicle environment comprising various speed and road conditions. Test results show that speedups up to 75 times can be achieved on parallel GPU implementation over sequential counterpart. As this speedup complies with real-time performance requirements, high computational cost of using map topology information with large number of particles in particle filter implementation is proven to be handled by emerging technologies. The effect of number of particles on error rate of localization and map matching is also investigated.