Headlight Range Estimation for Autonomous Driving using Deep Neural Networks

When driving at night, a good illumination of the road ahead is crucial. With autonomous driving at close temporal proximity, this not only concerns human drivers but also autonomous systems capable of controlling the car. To achieve fully autonomous driving, a variety of sensors are integrated into the vehicles. Cameras act as one of the major sensors. However, due to their passivity, cameras cannot see well in the dark. To mitigate this shortcoming, modern cars are equipped with powerful headlights that provide proper illumination of the road ahead while avoiding the dazzling of other traffic participants. To use the headlights' full potential and to also provide advanced light functionality like glare-free high beam, they need to be properly adjusted. After the initial calibration during production, this setting is prone to undesirable degradation, primarily due to mechanical reasons. We present a completely new application of computer vision and machine learning to automatically detect wrongly adjusted headlights by estimating their pitch angle from the images of a vehicle-attached camera for advanced driving assistance systems (ADAS). We show that we can achieve high performance in terms of accuracy and robustness by training a deep neural network in an end-to-end fashion. To demonstrate the benefits of our proposed approach, an additional handcrafted baseline method is implemented.