The R-Car V3H SoCs deliver a combination of high computer vision performance and artificial intelligence processing at low power levels, providing an optimized embedded solution for automotive front cameras in Level 2+ autonomous vehicles.
The reference software covers input from sensor or recorded data, all stages of processing and display output on a screen.
Renesas designed the SoCs with dedicated hardware accelerators for key algorithms including convolutional neural networks, dense optical flow, stereo disparity, and object classification.
The COD (
camera obstacle detection ) reference software uses convolutional neural network (CNN) IP, a computer vision engine (CV-E), and image rendering (IMR) technology to detect 2D objects such as cars, trucks, buses, and pedestrians. The COD achieves approximately 30 frames per second (FPS).
The LOD (
LiDAR obstacle detection ) software uses CNN-IP and CV-E to detect 3D objects, including cars and trucks. The LOD achieves approximately 15 FPS with 3D bounding boxes at 50 meters.
road feature detection ) RFD reference software uses CNN-IP, CV-E, IMR, and a versatile pipeline engine (IMP) to identify drivable free space, lanes (crossable and uncrossable), road boundaries, and distances to lanes and nearest objects to support NCAP 2020. The RFD achieves approximately 30 FPS.