Accelerated on-Chip Algorithm Based on Semantic Region-Based Partial Difference Detection for LiDAR-Vision Depth Data Transmission Reduction in Lightweight Controller Systems of Autonomous Vehicle

LiDAR sensors are one type of sensor used in autonomous driving vehicles that obtain distance data through the flight time of light. A LiDAR sensor can measure data at high speeds, and the precision of the data is higher than with other sensors. A large amount of data per sensing time is transmitted from sensors. Autonomous driving vehicles use man electronic devices, so the data channels they use and the domain control unit resources that control the system are limited. In this environment, if LiDAR sensor data can be reduced without compromising the original data, it can have a quite positive impact on autonomous vehicle systems. In this paper, we propose a differential partial update for data reduction of LiDAR sensors and a semantic detection to eliminate the resulting noise and increase the reliability of the data. The sensor processor extracts only the changed parts of the continuous distance data, excluding the same parts, and transmit them to the host. The high-difference noise is eliminated by filtering through a window-sliding operation. Semantic detection marks only parts that change and detects movement in the field of view. Simple differential partial updates reduce the amount of data by 59.31% based on a simple case. A semantic detection partial update can reduce the amount of data by 83.41%. This process can also reduce computing time by 61.36% with graphics processing unit acceleration.