Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving
This addresses the challenge of sensor failure in bad weather for autonomous vehicles, but it is incremental as it focuses on benchmarking rather than introducing a new sensor or algorithm.
The paper tackles the problem of sensor reliability in autonomous driving under adverse weather by developing a benchmarking methodology, showing that gated imaging outperforms standard passive imaging in foggy conditions due to active illumination.
Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develop robust sensors and algorithms, tests with current sensors in defined weather conditions are crucial for determining the impact of bad weather for each sensor. This work describes a testing and evaluation methodology that helps to benchmark novel sensor technologies and compare them to state-of-the-art sensors. As an example, gated imaging is compared to standard imaging under foggy conditions. It is shown that gated imaging outperforms state-of-the-art standard passive imaging due to time-synchronized active illumination.