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.
We propose a Partially Observable Markov Decision Process framework and employ the Trust Region Policy Optimization algorithm to train our agent.
Engineering 5.5 · Research 8.0 · Business 5.5
Finally, we present the possible future trends of RV fusion and summarize this paper.
Engineering 5.5 · Research 7.0 · Business 5.0
In this paper, we introduce ICOP, an end-to-end driving system based on multi-agent camera cooperative perception, to select sensor sharing nodes and to fuse intermediate image data features for learning a driving policy.
Engineering 5.5 · Research 7.0 · Business 5.0
Few prior works study self-driving cars by deep learning with IoT collaboration.
Engineering 5.5 · Research 8.0 · Business 5.0
To this end, we explore the sparse representation and review the task design for end-to-end autonomous driving, proposing a new paradigm named SparseDrive.
Engineering 5.5 · Research 8.0 · Business 5.0
To address these issues, we propose a diffusion-based 4D occupancy generation model, OccSora, to simulate 3D world evolution for autonomous driving.
Engineering 5.5 · Research 7.0 · Business 5.0
To facilitate efficient 3D scene reconstruction without costly annotations, we propose a self-supervised street Gaussian ($\textit{S}^3$Gaussian) method to decompose dynamic and static elements from 4D consistency.
Engineering 7.5 · Research 7.0 · Business 6.5
We propose a rigorous test method based on breaking down scenarios into simple ones, taking into account the fact that autopilots make decisions according to traffic rules whose application depends on local knowledge and context.
Engineering 5.0 · Research 7.0 · Business 6.0
To address this, we propose an object-centric representation to describe 3D scenes with sparse 3D semantic Gaussians where each Gaussian represents a flexible region of interest and its semantic features.
Engineering 7.0 · Research 8.0 · Business 5.0
In this study, we present RoboBEV, an extensive benchmark suite designed to evaluate the resilience of BEV algorithms.
Engineering 5.5 · Research 8.0 · Business 5.5
To address these issues, this paper proposes a self-attention-based bidirectional long short-term memory (Att-Bi-LSTM) network model to predict driving risk based on multi-source data.
Engineering 5.0 · Research 8.0 · Business 5.0
An autonomous driving research paper: Automated Parking Planning with Vision-Based BEV Approach.
Engineering 5.0 · Research 7.0 · Business 5.0
To improve perception robustness, we leverage the recent advances in automotive radars and introduce a novel approach that utilizes 4D imaging radar sensors for 3D occupancy prediction.
Engineering 5.5 · Research 8.0 · Business 6.0
Given the constrained computational capacities of onboard vehicular computers, we introduce a compact yet potent solution named EfficientFuser.
Engineering 6.0 · Research 8.0 · Business 6.0
This paper proposes a novel spatiotemporal BEV pyramid network which employs Swin Transformer to extract BEV features transformed by images and predict across multiple scales.
Engineering 5.5 · Research 8.0 · Business 5.0
In this paper, we introduce a novel multitask imitation learning framework for end-to-end autonomous driving that leverages a dual attention transformer (DualAT) to enhance the multimodal fusion and waypoint prediction processes.
Engineering 5.5 · Research 8.0 · Business 5.0
Finally, integrating our findings, we propose a strong baseline model—PlanTF.
Engineering 7.5 · Research 8.0 · Business 5.5
The field of autonomous driving technology is rapidly advancing, with deep learning being a key component.
Engineering 5.0 · Research 8.0 · Business 5.0
Accurate navigation prediction is paramount for autonomous driving, offering the potential to enhance safety and efficiency by mitigating accidents caused by human error.
Engineering 6.0 · Research 8.0 · Business 5.5
In this paper, we address this challenge by introducing a world model-based autonomous driving 4D representation learning framework, dubbed DriveWorld, which is capable of pretraining from multi-camera driving videos in a spatiotemporal fashion.
Engineering 5.0 · Research 7.0 · Business 5.0