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 introduce a systematic missing-view evaluation protocol on the nuScenes-based SurroundOcc benchmark, encompassing both deterministic single-view failures and stochastic multi-view dropout scenarios.
Engineering 7.5 · Research 7.0 · Business 6.5
Large language models (LLMs) exhibit strong semantic reasoning capabilities for autonomous driving decision-making; however, their substantial inference latency poses a critical challenge for real-time closed-loop vehicle control.
Engineering 5.5 · Research 7.5 · Business 5.0
The rapid evolution of autonomous driving has made high-fidelity simulation essential for validating safety-critical algorithms.
Engineering 7.5 · Research 7.0 · Business 5.5
Autonomous robotic systems are increasingly deployed across manufacturing, logistics, and healthcare due to their ability to efficiently complete difficult and dangerous tasks.
Engineering 5.0 · Research 8.0 · Business 5.0
To address this and advance sustainable autonomous driving, this paper proposes a Bird’s-Eye View (BEV)-based multi-modal 3D object detection approach tailored for nighttime scenarios, integrating low-light adaptive components while preserving the original BEV pipeline.
Engineering 6.0 · Research 8.0 · Business 6.0
To address these challenges, we propose Dr.
Engineering 5.5 · Research 7.0 · Business 5.0
To address this, we introduce a novel task: unobserved road segmentation, focusing on explicitly identifying road regions occluded from the ego-vehicle’s view.
Engineering 5.0 · Research 8.0 · Business 5.0
To address this, we propose leveraging Vehicle-to-Everything (V2X) communication to partially offload processing to the cloud, where compute resources are abundant, thus reducing overall latency.
Engineering 6.0 · Research 7.0 · Business 5.5
The objective of this project is to build up sophisticated autonomous driving simulation based on CARLA platform and reinforcement learning.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose a novel Privacy-Concealing Cooperation (PCC) framework for Bird's Eye View (BEV) semantic segmentation.
Engineering 5.5 · Research 8.0 · Business 5.0
To address these challenges, we propose HiST-VLA, a novel Hierarchical Spatio-Temporal VLA model designed for reliable trajectory generation.
Engineering 5.5 · Research 8.0 · Business 5.0
To this end, we propose DriveMamba, a Task-Centric Scalable paradigm for efficient E2E-AD, which integrates dynamic task relation modeling, implicit view correspondence learning and long-term temporal fusion into a single-stage Unified Mamba decoder.
Engineering 6.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: Enhancing Perception: A Comparative Analysis of PIXOR and MV3D for Four-Wheeler Detection in Autonomous Vehicles.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these issues, we propose AppleVLM, an advanced perception and planning-enhanced VLM model for robust end-to-end driving.
Engineering 6.0 · Research 8.0 · Business 6.5
This paper presents the mechanical design, sensor fusion strategy, path planning algorithms, and experimental validation of AGNI in challenging terrains.
Engineering 5.0 · Research 7.0 · Business 5.0
Precise sensor integration is crucial for autonomous vehicle (AV) navigation, yet traditional extrinsic calibration remains costly and labor-intensive.
Engineering 5.5 · Research 7.0 · Business 5.5
To address this, we propose a novel framework named Effective and Robust Collaborative Perception (ERCP), which is designed to enhance feature fusion with strong robustness against CAV pose errors.
Engineering 5.0 · Research 8.0 · Business 5.0
In this paper, we explore a frequency domain BEV representation to address these challenges and propose the FreqBEV-V2I framework that incorporates FreqBEVFlow and FreqBEVFusion blocks.
Engineering 7.0 · Research 8.0 · Business 5.5
An autonomous driving research paper: Point cloud-based multi-target 3D object detection using LiDAR sensor and deep learning.
Engineering 5.0 · Research 7.0 · Business 5.0
Autonomous underwater vehicle (AUV) motion planning in complex three-dimensional ocean environments remains challenging due to the simultaneous requirements of obstacle avoidance, dynamic feasibility, and energy efficiency.
Engineering 5.5 · Research 7.0 · Business 5.5