190 papers with code • 1 benchmarks • 24 datasets
Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.
We then directly transfer this policy without any tuning to the University of Delaware Scaled Smart City (UDSSC), a 1:25 scale testbed for connected and automated vehicles.
The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility.
Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications.
The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.
Ranked #10 on Multiple Object Tracking on KITTI Tracking test
And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.
In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.