Autonomous Vehicles
539 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
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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
Subtasks
Latest papers
Active propulsion noise shaping for multi-rotor aircraft localization
Multi-rotor aerial autonomous vehicles (MAVs) primarily rely on vision for navigation purposes.
Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving
Large Language Models (LLMs) have garnered significant attention for their ability to understand text and images, generate human-like text, and perform complex reasoning tasks.
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments
With extensive experiments, PC-NeRF is proven to achieve high-precision novel LiDAR view synthesis and 3D reconstruction in large-scale scenes.
MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLO
shubha07m/On-device-computer-vision-experiments-with-IoT
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Low-light conditions and occluded scenarios impede object detection in real-world Internet of Things (IoT) applications like autonomous vehicles and security systems.
AoSRNet: All-in-One Scene Recovery Networks via Multi-knowledge Integration
Additionally, we suggest a multi-receptive field extraction module (MEM) to attenuate the loss of image texture details caused by GC nonlinear and OLS linear transformations.
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
hkust-aerial-robotics/simpl
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This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles.
Can you see me now? Blind spot estimation for autonomous vehicles using scenario-based simulation with random reference sensors
In this paper, we introduce a method for estimating blind spots for sensor setups of autonomous or automated vehicles and/or robotics applications.
Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation
ruipingl/miss
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Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory framework.
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
To our knowledge, for the very first time in lidar-based 3D detection tasks, the PTQ INT8 model's accuracy is almost the same as the FP32 model while enjoying $3\times$ inference speedup.
SGV3D:Towards Scenario Generalization for Vision-based Roadside 3D Object Detection
yanglei18/sgv3d
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Our method surpasses all previous methods by a significant margin in new scenes, including +42. 57% for vehicle, +5. 87% for pedestrian, and +14. 89% for cyclist compared to BEVHeight on the DAIR-V2X-I heterologous benchmark.
