Browse > Computer Vision > Autonomous Vehicles

Autonomous Vehicles

44 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.

State-of-the-art leaderboards

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

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

26 Mar 2018NVIDIA-Jetson/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

Joint 3D Proposal Generation and Object Detection from View Aggregation

6 Dec 2017kujason/avod

We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.

3D OBJECT DETECTION AUTONOMOUS DRIVING

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

nuScenes: A multimodal dataset for autonomous driving

26 Mar 2019nutonomy/nuscenes-devkit

In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.

AUTONOMOUS DRIVING OBJECT DETECTION

Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control

16 Oct 2017flow-project/flow

Flow is a new computational framework, built to support a key need triggered by the rapid growth of autonomy in ground traffic: controllers for autonomous vehicles in the presence of complex nonlinear dynamics in traffic.

AUTONOMOUS VEHICLES

Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters

CVPR 2017 huangshiyu13/RPNplus

Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult.

PEDESTRIAN DETECTION

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

14 Jun 2018BertaBescos/DynaSLAM

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.

AUTONOMOUS VEHICLES OBJECT DETECTION

Embedded real-time stereo estimation via Semi-Global Matching on the GPU

13 Oct 2016dhernandez0/sgm

Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles.

AUTONOMOUS VEHICLES DISPARITY ESTIMATION

LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking

19 Apr 2019bernwang/latte

2) One-click annotation: Instead of drawing 3D bounding boxes or point-wise labels, we simplify the annotation to just one click on the target object, and automatically generate the bounding box for the target.

AUTONOMOUS VEHICLES SENSOR FUSION