Hao Zheng, co-founder and CEO of RoboK, said, “Advanced driving features, which range from collision avoidance and automatic lane-keeping through to fully automated driving, require miles of road test driving to ensure their safety. Although simulation provides a resource-efficient alternative it can be time-consuming. To accurately and realistically simulate all elements in the entire system it can take many hours to run and process even a single driving scenario.
“We have reduced the computation time by developing a significant new method for fusing raw data directly from a range of sensors, such as cameras, radars, GPS and IMU, as well as for performing depth estimation to gain 3D information, all running on low-power computing platforms. This significantly reduces the memory and computing requirement. When this is combined with our novel and highly optimized AI-based perception modules, intelligent insights can be gained rapidly and efficiently, which is vital for fast decision-making.”
A large proportion of traffic accidents are due to human error, but the cost and complexity of developing next-generation advanced driver assistance systems (ADAS) means they are often unavailable on lower-cost vehicles.