5 accepted papers
Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are traine…
Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay. Understanding the uncertainty of the prediction is also crucial. Most existing approaches however can only estimate uncertainty through repeated…
Combining multiple sensors for advanced perception is a crucial requirement for autonomous vehicle navigation. Heterogeneous sensors are used to obtain rich information about the surrounding environment. The combination of the camera and lidar sensors enables precise range information that can be pr…
Vision systems are widely used in autonomous vehicle systems due to the rich information that camera sensors provide of the surrounding environment. This paper presents an automatic algorithm to obtain the drivable path of a vehicle operating in urban roads with or without clear lane markings. The d…
To navigate through urban roads, an automated vehicle must be able to perceive and recognize objects in a three-dimensional environment. A high level contextual understanding of the surroundings is necessary to execute accurate driving maneuvers. This paper presents a novel approach to build three d…