  Autonomous driving applications use two types of sensor systems to identify
vehicles - depth sensing LiDAR and radiance sensing cameras. We compare the
performance (average precision) of a ResNet for vehicle detection in complex,
daytime, driving scenes when the input is a depth map (D = d(x,y)), a radiance
image (L = r(x,y)), or both [D,L]. (1) When the spatial sampling resolution of
the depth map and radiance image are equal to typical camera resolutions, a
ResNet detects vehicles at higher average precision from depth than radiance.
(2) As the spatial sampling of the depth map declines to the range of current
LiDAR devices, the ResNet average precision is higher for radiance than depth.
(3) For a hybrid system that combines a depth map and radiance image, the
average precision is higher than using depth or radiance alone. We established
these observations in simulation and then confirmed them using realworld data.
The advantage of combining depth and radiance can be explained by noting that
the two type of information have complementary weaknesses. The radiance data
are limited by dynamic range and motion blur. The LiDAR data have relatively
low spatial resolution. The ResNet combines the two data sources effectively to
improve overall vehicle detection.
