Improved and Efficient Inter-Vehicle Distance Estimation Using Road Gradients of Both Ego and Target Vehicles
Muhyun Back, Jin-Kyu Lee, Kyuho Bae, Sung Soo Hwang, Il Yong Chun · 2021
In advanced driver assistant systems and autonomous driving, it is crucial to estimate distances between an ego vehicle and target vehicles. Existing inter-vehicle distance estimation methods assume that the ego and target vehicles drive on a same ground plane. In practical driving environments, however, they may drive on different ground planes. This paper proposes an inter-vehicle distance estimation framework that can consider slope changes of a road forward, by estimating road gradients of both ego vehicle and target vehicles and using a 2D object detection deep net. Numerical experiments demonstrate that the proposed method significantly improves the distance estimation accuracy and time complexity, compared to deep learning-based depth estimation methods.