Autonomous Driving
1410 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
A Point-Based Approach to Efficient LiDAR Multi-Task Perception
Unlike other LiDAR-based multi-task architectures, our proposed PAttFormer does not require separate feature encoders for multiple task-specific point cloud representations, resulting in a network that is 3x smaller and 1. 4x faster while achieving competitive performance on the nuScenes and KITTI benchmarks for autonomous driving perception.
Camera Agnostic Two-Head Network for Ego-Lane Inference
Vision-based ego-lane inference using High-Definition (HD) maps is essential in autonomous driving and advanced driver assistance systems.
Dragtraffic: A Non-Expert Interactive and Point-Based Controllable Traffic Scene Generation Framework
However, most existing scene generation methods lack controllability, accuracy, and versatility, resulting in unsatisfactory generation results.
BACS: Background Aware Continual Semantic Segmentation
Besides the common problem of classical catastrophic forgetting in the continual learning setting, CSS suffers from the inherent ambiguity of the background, a phenomenon we refer to as the "background shift'', since pixels labeled as background could correspond to future classes (forward background shift) or previous classes (backward background shift).
An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles
Among these modules, the trajectory planner plays a pivotal role in the safety of the vehicle and the comfort of its passengers.
Reducing Bias in Pre-trained Models by Tuning while Penalizing Change
If later such a bias is discovered during inference or deployment, it is often necessary to acquire new data and retrain the model.
S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles
To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework.
Stability Certificates for Receding Horizon Games
Game-theoretic MPC (or Receding Horizon Games) is an emerging control methodology for multi-agent systems that generates control actions by solving a dynamic game with coupling constraints in a receding-horizon fashion.
TrACT: A Training Dynamics Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
In this paper, we propose to incorporate richer training dynamics information into a prototypical contrastive learning framework.
SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation
Our approach represents a significant leap forward in self-supervised monocular depth estimation, underscoring the importance of strengthening pose information for advancing scene understanding in real-world applications.
