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.
To overcome this issue, we propose Context Aware Scene Prediction Transformer (CASPFormer), which can perform multi-modal motion prediction from rasterized Bird-Eye-View (BEV) images.
Engineering 6.0 · Research 8.0 · Business 7.0
In this paper, we investigate the security risks associated with monocular vision-based depth estimation algorithms utilized by AD systems.
Engineering 5.5 · Research 8.0 · Business 5.5
In this paper, we taxonomize the leading targetless calibration methods into three categories based on their underlying algorithms, namely feature, information theory, and learning-based methods.
Engineering 5.0 · Research 8.0 · Business 5.0
We propose a test environment for vision-based autonomous driving functions in which a real camera and a deep learning model can be evaluated jointly.
Engineering 6.0 · Research 8.0 · Business 5.5
To tackle this issue, we introduce the Efficient Axial Transformer Network (EATNet), a lightweight multi-modal autonomous driving framework based on cross-axial Transformers.
Engineering 6.0 · Research 7.0 · Business 6.5
An autonomous driving research paper: Automatic Multi-Sensor Dataset Generation in Autonomous Vehicle Environments.
Engineering 5.0 · Research 7.0 · Business 5.0
Existing sensor fusion approaches are poor, this paper proposes a novel end-to-end autonomous driving model called M2FU.
Engineering 5.5 · Research 8.0 · Business 5.0
An autonomous driving research paper: An autonomous driving car-following algorithm based on optimized TD3 and its simulation.
Engineering 5.0 · Research 7.0 · Business 5.0
In response, we introduce RopeBEV, the first dense multi-camera BEV approach.
Engineering 5.5 · Research 7.0 · Business 5.5
In this paper, we propose an integrated trajectory planning and control method to achieve time-optimal autonomous patrol of USVs by a data-efficient iterative learning-based predictive control algorithm.
Engineering 5.0 · Research 7.0 · Business 5.0
To this end, we present a novel perspective on the occupancy prediction task: formulating it as a streamlined set prediction paradigm without the need for explicit space modeling or complex sparsification procedures.
Engineering 6.0 · Research 8.0 · Business 5.0
In this paper, we provide a review of 3D object detection methods for multi-sensor fusion.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper proposes a self-supervised monocular depth estimation (SS-MDE) model suitable for UAV by improving the accuracy of depth estimation.
Engineering 5.0 · Research 8.0 · Business 5.0
More specifically, researchers have combined Transformers with Imitation Learning, in order to construct agents that learn to map navigation states to actions from large datasets created by experts.
Engineering 5.5 · Research 7.0 · Business 5.0
In this work, we introduce a novel LiDAR 3D object detection framework, namely LiSTM, to facilitate spatial-temporal feature learning with cross-frame motion forecasting information.
Engineering 5.5 · Research 8.0 · Business 6.0
Here, we present a compact meta-imaging camera and an analytical framework for the quantification of monocular depth sensing precision by calculating the Cramér–Rao lower bound of depth estimation.
Engineering 5.0 · Research 7.0 · Business 5.0
To this end, we propose OccLLaMA, an occupancy-language-action generative world model, which uses semantic occupancy as a general visual representation and unifies vision-language-action(VLA) modalities through an autoregressive model.
Engineering 5.0 · Research 8.5 · Business 5.0
Therefore, the objective of our approach is to reduce the aforementioned performance drop as much as possible using a modern multi-camera surround view model reduced for single-camera inference.
Engineering 5.0 · Research 8.0 · Business 6.0
An autonomous driving research paper: LWTD: a novel light-weight transformer-like CNN architecture for driving scene dehazing.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: Lane detection based on real-time semantic segmentation for end-to-end autonomous driving under low-light conditions.
Engineering 5.5 · Research 7.0 · Business 5.0