Browse curated autonomous driving papers on end-to-end driving, BEV perception, 3D object detection, motion prediction, path planning, ADAS, Tesla FSD, Waymo, and self-driving foundation models.
However, this cue is highly susceptible to overfitting, thus we propose a novel canonical representation that maintains consistency across varied camera setups, effectively disentangling depth from specific parameters and enhancing generalization across datasets.
Engineering 6.5 · Research 8.0 · Business 5.0
Autonomous driving systems depend on various perception tasks to ensure a clear understanding of the road, requiring accuracy and speed.
Engineering 5.0 · Research 7.0 · Business 5.0
To address this, we propose a probabilistic Gaussian superposition model which interprets each Gaussian as a probability distribution of its neighborhood being occupied and conforms to probabilistic multiplication to derive the overall geometry.
Engineering 5.5 · Research 8.0 · Business 5.0
We propose STATIC, a novel model that independently learns temporal consistency in static and dynamic area without additional information.
Engineering 5.0 · Research 8.0 · Business 5.0
In the past few decades, autonomous driving algorithms have made significant progress in perception, planning, and control.
Engineering 5.5 · Research 8.0 · Business 6.0
Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation.
Engineering 5.0 · Research 7.0 · Business 5.0
By merging cutting-edge visual odometry techniques with robust control theory, our approach enhances both the safety and performance of AVs in complex traffic situations, directly addressing the challenge of safe and adaptive navigation.
Engineering 5.5 · Research 8.0 · Business 5.5
Reinforcement learning (RL) shows promise for autonomous driving decision-making.
Engineering 5.5 · Research 8.0 · Business 5.5
Improving the ability of autonomous vehicles to accurately identify and follow lanes in various contexts is crucial.
Engineering 5.5 · Research 8.0 · Business 5.5
3D object detection is a crucial feature of autonomous driving systems for localizing and categorizing objects.
Engineering 5.0 · Research 7.0 · Business 5.0
We propose the Dual Window-based Cross-Attention (DWCA) model which extracts and fuses selected camera and LiDAR data features and projects that onto a Bird's-Eye View (BEV) representation on a single ego vehicle.
Engineering 5.0 · Research 8.0 · Business 5.0
Here, we introduce the Concept-Wrapper Network (CW-Net), a method for explaining the behavior of machine-learning-based planners by grounding their reasoning in human-interpretable concepts.
Engineering 5.5 · Research 7.0 · Business 6.5
In this paper, we explore the potential of leveraging Large Language Models (LLMs) for automated test generation based on free-form textual descriptions in area of automotive.
Engineering 6.5 · Research 8.5 · Business 5.0
Specifically, they ignore the feature-blurring problem and do not deeply interact with image semantic information.
Engineering 5.0 · Research 8.0 · Business 5.0
The core component of Advanced Driver Assistance Systems (ADAS) is the perception module, which has been a primary focus for enhancing robustness and quality against various environmental conditions like changing lighting and weather.
Engineering 5.0 · Research 7.0 · Business 5.5
Currently, autonomous driving technology is rapidly developing.
Engineering 5.0 · Research 8.0 · Business 5.0
To address these challenges, we propose a novel truncated diffusion policy that incorporates prior multi-mode anchors and truncates the diffusion schedule, enabling the model to learn denoising from anchored Gaussian distribution to the multi-mode driving action distribution.
Engineering 5.5 · Research 8.0 · Business 5.0
To address these issues, we propose MagicDriveV2, a novel approach that integrates the MVDiT block and spatial-temporal conditional encoding to enable multiview video generation and precise geometric control.
Engineering 6.5 · Research 8.0 · Business 5.0
To tackle these issues, we propose a novel method for 3D object detection based on Sparse Feature Extraction and Semantic Fusion from LiDAR point clouds.
Engineering 5.5 · Research 8.0 · Business 5.0
We propose perspective-aware hierarchical vision transformer-based LiDAR-camera fusion (PLC-Fusion) for 3D object detection to address this.
Engineering 5.0 · Research 8.0 · Business 5.0