Autonomous driving is the task of driving a vehicle without human conduction.
( Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving )
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This assumption greatly simplifies the learning problem, factorizing the dynamics into a nonreactive world model and a low-dimensional and compact forward model of the ego-vehicle.
Ranked #1 on Autonomous Driving on CARLA Leaderboard
3D object detection with a single image is an essential and challenging task for autonomous driving.
In this technical report, we study this problem with a practice built on fully convolutional single-stage detector and propose a general framework FCOS3D.
Ranked #41 on 3D Object Detection on nuScenes
We further design a re-weighting strategy to handle the inaccuracy caused by misalignment between day-night image pairs and wrong predictions of daytime images, as well as boost the prediction accuracy of small objects.
Ranked #1 on Semantic Segmentation on Dark Zurich
FIERY learns to model the inherent stochastic nature of the future directly from camera driving data in an end-to-end manner, without relying on HD maps, and predicts multimodal future trajectories.
Ranked #1 on Bird's-Eye View Semantic Segmentation on nuScenes
This technology enables drivers to use voice commands to control the vehicle and will be soon available in Advanced Driver Assistance Systems (ADAS).
How should representations from complementary sensors be integrated for autonomous driving?
Ranked #1 on Autonomous Driving on Town05 Short
3D object detection and dense depth estimation are one of the most vital tasks in autonomous driving.
The experimental results show that our method achieves the state-of-the-art performance on the monocular 3D Object detection and Birds Eye View tasks on the KITTI dataset, and can generalize to images with different camera intrinsics.
Finally, we design an ensemble model to combine the strengths of the different learning strategies.