Vehicle recognition using common appearance captured by 3D LIDAR and monocular camera

In driving environments, other vehicles are one of the most frequently appearing close range objects from the ego-vehicle. Thus, the development of high accuracy vehicle recognition algorithms is essential for safe and efficient automated driving. However, detecting vehicles with consistently high accuracy is difficult because there are various vehicle types with different appearances, such as sedans, buses, trucks, and SUVs. This intra-class variation must be addressed or, irregular recognition performance can occur, depending on vehicle type. Conventional machine learning-based algorithms are inadequate to address this problem because they are mostly trained on samples of entire appearance. Considering the wide variability in vehicle appearance, collecting samples of every vehicle type may not be ideal. In this study, we propose a vehicle recognition algorithm using common appearance characteristics of every vehicle type. Rectangular shapes are captured by a 3D LIDAR while tires and bumpers are captured by a monocular camera. Angular features extracted from these common appearances are then fused by the Dempster-Shafer theory framework for vehicle recognition. By performing real-world experiments, we demonstrated that common appearances captured by the proposed algorithm provide sufficiently generalized features to recognize diverse vehicle types in urban driving environments.