Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
The last decades have witnessed the breakthrough of autonomous vehicles (AVs), and the perception capabilities of AVs have been dramatically improved.
LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot's pose and build high-precision, high-resolution maps of the surrounding environment.
Such systems call for early and accurate prediction of a pedestrian's crossing/not-crossing behaviour in front of the vehicle.
In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented.
The Crossing or Not-Crossing (C/NC) problem is important to autonomous vehicles (AVs) for safe vehicle/pedestrian interactions.
Lane detection is extremely important for autonomous vehicles.
Autonomous vehicles (AV) are expected to navigate in complex traffic scenarios with multiple surrounding vehicles.
We use 3D object tracking to mine for more than 300k interesting vehicle trajectories to create a trajectory forecasting benchmark.