Autonomous Driving
1424 papers with code • 4 benchmarks • 66 datasets
Autonomous driving is the task of driving a vehicle 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: Exploring the Limitations of Behavior Cloning for Autonomous Driving)
Benchmarks
These leaderboards are used to track progress in Autonomous Driving
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Libraries
Use these libraries to find Autonomous Driving models and implementationsDatasets
CARLA
Waymo Open Dataset
AirSim
Virtual KITTI
TORCS
IDD
INTERACTION Dataset
ApolloScape
SemanticPOSS
WoodScape
Latest papers with no code
ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties
However, the significant time consumption and sensitivity to noise have limited the real-time predictive capability of diffusion models.
Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection
Specifically, in the selection process, to improve the reliability of pseudo boxes, we propose a complementary augmentation strategy.
SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphs
Trajectory prediction in autonomous driving relies on accurate representation of all relevant contexts of the driving scene including traffic participants, road topology, traffic signs as well as their semantic relations to each other.
Guiding Attention in End-to-End Driving Models
Vision-based end-to-end driving models trained by imitation learning can lead to affordable solutions for autonomous driving.
STT: Stateful Tracking with Transformers for Autonomous Driving
In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately.
SemVecNet: Generalizable Vector Map Generation for Arbitrary Sensor Configurations
In response to this challenge, we propose a modular pipeline for vector map generation with improved generalization to sensor configurations.
$ν$-DBA: Neural Implicit Dense Bundle Adjustment Enables Image-Only Driving Scene Reconstruction
The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of bundle adjustment (BA), essential for autonomous driving.
Safe Reach Set Computation via Neural Barrier Certificates
We present a novel technique for online safety verification of autonomous systems, which performs reachability analysis efficiently for both bounded and unbounded horizons by employing neural barrier certificates.
RadSimReal: Bridging the Gap Between Synthetic and Real Data in Radar Object Detection With Simulation
Object detection in radar imagery with neural networks shows great potential for improving autonomous driving.
Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation
Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles.
