Real-time power-efficient integration of multi-sensor occupancy grid on many-core

Abstract

Safe Autonomous Vehicles (AVs) will emerge when comprehensive perception systems will be successfully integrated into vehicles. Advanced perception algorithms based on occupancy grids were developed for AV prototypes. These algorithms estimate the position and speed of every obstacle in the environment by using data fusion from multiple sensors. Computational requirements of such fusion prevent their integration into AVs on current low-power embedded hardware. However, recent emerging many-core architectures offer opportunities to fulfill the automotive market constraints and efficiently support advanced perception applications. This paper explores the integration of the occupancy grid multi-sensor fusion algorithm into low power many-core architectures. The parallel properties of this function are used to achieve realtime performance with a power consumption less than 1W. The proposed implementation produces an occupancy grid of 500×300 cells within 6.26ms. The execution time is 6x faster than typical sensor output rates and 9x faster than previous embedded prototypes.