Browse SoTA > Computer Vision > Autonomous Vehicles

Autonomous Vehicles

101 papers with code · Computer Vision

Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

( Image credit: AirSim )

Benchmarks

You can find evaluation results in the subtasks. You can also submitting evaluation metrics for this task.

Greatest papers with code

AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

15 May 2017Microsoft/AirSim

Developing and testing algorithms for autonomous vehicles in real world is an expensive and time consuming process.

AUTONOMOUS VEHICLES

Accelerating 3D Deep Learning with PyTorch3D

16 Jul 2020facebookresearch/pytorch3d

We address these challenges by introducing PyTorch3D, a library of modular, efficient, and differentiable operators for 3D deep learning.

AUTONOMOUS VEHICLES

nuScenes: A multimodal dataset for autonomous driving

CVPR 2020 traveller59/second.pytorch

Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.

Ranked #3 on 3D Object Detection on nuScenes (using extra training data)

3D OBJECT DETECTION AUTONOMOUS DRIVING

On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

26 Mar 2018NVIDIA-AI-IOT/redtail

Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving.

AUTONOMOUS VEHICLES STEREO DEPTH ESTIMATION

Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving

ICCV 2019 jwchoi384/Gaussian_YOLOv3

Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications.

AUTONOMOUS DRIVING OBJECT DETECTION

LiDAR-Camera Calibration using 3D-3D Point correspondences

27 May 2017ankitdhall/lidar_camera_calibration

With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors.

AUTONOMOUS VEHICLES

Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV

15 Sep 2019hku-mars/loam_livox

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.

AUTONOMOUS NAVIGATION

Flow: A Modular Learning Framework for Autonomy in Traffic

16 Oct 2017flow-project/flow

To enable the study of the full diversity of traffic settings, we first propose to decompose traffic control tasks into modules, which may be configured and composed to create new control tasks of interest.

AUTONOMOUS VEHICLES

Joint Monocular 3D Vehicle Detection and Tracking

ICCV 2019 ucbdrive/3d-vehicle-tracking

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.

3D OBJECT DETECTION 3D POSE ESTIMATION AUTONOMOUS VEHICLES MULTIPLE OBJECT TRACKING ONLINE MULTI-OBJECT TRACKING TRAJECTORY PREDICTION