high performance computing on graphics processing units: hgpu.org

When designing a safety system, the faster the response time, the greater the reflexes of the system to hazards. As more commercial interest in autonomous and assisted vehicles grows, the number one concern is safety. If the system cannot react as fast as or faster than an average human, then the public will deem it unsafe. In this thesis, I explore the feasibility of using GPU hardware to perform the algorithms used for determining robotic obstacle avoidance. These obstacle avoidance algorithms are ideally suited to reacting to emergency hazards. The product of this research will be a libarary of OpenCL accelerated functions designed for processing environmental data from LiDAR sensors. The results show that by adopting algorithms to take advantage of the parallel architecture of GPUs, processing times significantly decrease for large data sets.