Autonomous Vehicles
532 papers with code • 1 benchmarks • 27 datasets
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: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )
Benchmarks
These leaderboards are used to track progress in Autonomous Vehicles
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find Autonomous Vehicles models and implementationsDatasets
CARLA
AirSim
INTERACTION Dataset
Talk2Car
RadarScenes
ROAD
Drive&Act
ApolloCar3D
Argoverse 2 Motion Forecasting
DADA-2000
Subtasks
Latest papers with no code
VBR: A Vision Benchmark in Rome
This paper presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data.
How to deal with glare for improved perception of Autonomous Vehicles
In this paper, we investigate various glare reduction techniques, including the proposed saturated pixel-aware glare reduction technique for improved performance of the computer vision (CV) tasks employed by the perception layer of AVs.
Characterization and Mitigation of Insufficiencies in Automated Driving Systems
Automated Driving (AD) systems have the potential to increase safety, comfort and energy efficiency.
Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator
As Neural Processing Units (NPU) or accelerators are increasingly deployed in a variety of applications including safety critical applications such as autonomous vehicle, and medical imaging, it is critical to understand the fault-tolerance nature of the NPUs.
Benefits of V2V communication in connected and autonomous vehicles in the presence of delays in communicated signals
In particular, we relate this delay's effect on the selection of the time headway in predecessor-follower type vehicle platooning with a constant time headway policy (CTHP).
Depth Estimation using Weighted-loss and Transfer Learning
The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge Loss and Structural Similarity Index (SSIM).
Voice-Assisted Real-Time Traffic Sign Recognition System Using Convolutional Neural Network
Traffic signs are important in communicating information to drivers.
Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles
In this sense, explainability of real-time decisions is a crucial and natural requirement for building trust in autonomous vehicles.
Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication
To validate our approach, we used the CARLA simulator to create a dataset of annotated videos for different driving scenarios where pedestrian detection is challenging for an AV with compromised vision.
Label-Efficient 3D Object Detection For Road-Side Units
We address this challenge by devising a label-efficient object detection method for RSU based on unsupervised object discovery.
