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 address these issues, this paper proposes a framework that incorporates adversarial networks to constrain the encoder's output on top of Proximal Policy Optimization (PPO), combined with sensor fusion(ASF-PPO).
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we propose a camera-only perception framework that produces Bird's Eye View (BEV) maps by extending the Lift-Splat-Shoot architecture.
Engineering 5.5 · Research 7.0 · Business 5.0
An autonomous driving research paper: Intelligent Self-Driving Cars with Deep Learning and OpenCV: A Vision-Based Approach.
Engineering 5.0 · Research 7.0 · Business 5.0
In this paper, we introduce X-Driver, a unified multi-modal large language models(MLLMs) framework designed for closed-loop autonomous driving, leveraging Chain-of-Thought(CoT) and autoregressive modeling to enhance perception and decision-making.
Engineering 6.0 · Research 8.5 · Business 6.5
Robust Lane detection is critical for autonomous driving and high-end driver-assistance systems (ADAS) but remains challenging in adverse conditions.
Engineering 5.5 · Research 8.0 · Business 6.0
This paper proposes a novel roadside unit (RSU)-centric cooperative driving system leveraging global perception and vehicle-to-infrastructure (V2I) communication.
Engineering 5.0 · Research 8.0 · Business 5.0
We introduce DriveAgent, a modular multi-agent autonomous driving framework that leverages large language model (LLM) reasoning combined with multimodal sensor fusion for autonomous driving.
Engineering 5.0 · Research 8.5 · Business 5.0
Specifically, a structure completion module is designed to predict dense shapes of complete point clouds by leveraging sequence transduction ability of the transformer architecture.
Engineering 5.5 · Research 7.0 · Business 6.0
To that end, we introduce LightEMMA, a Lightweight End-to-End Multimodal Model for Autonomous driving.
Engineering 7.5 · Research 8.0 · Business 6.0
To bridge this gap, we propose a novel Frequency Self-Adaptation Graph Neural Network for Unsupervised Graph Anomaly Detection (FAGAD).
Engineering 7.0 · Research 8.0 · Business 5.5
Autonomous Vehicles (AVs) require precise lane and object detection to ensure safe navigation.
Engineering 5.0 · Research 7.0 · Business 5.0
Lane detection stands as a critical element in autonomous driving, ensuring vehicles navigate safely by accurately recognizing lane boundaries.
Engineering 5.0 · Research 7.0 · Business 5.0
A trajectory planning method is proposed to address the lane-changing problem in intelligent vehicles.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: Road Similarity-Based BEV-Satellite Image Matching for UGV Localization.
Engineering 5.0 · Research 7.0 · Business 5.0
In this work, we present FlowDep, an efficient and optical flowbased algorithm for depth estimation.
Engineering 5.5 · Research 7.0 · Business 6.0
This paper proposes an end-to-end reinforcement learning framework that integrates large-scale deep neural networks with advanced policy optimization techniques to improve decision-making in autonomous driving.
Engineering 5.5 · Research 8.0 · Business 5.0
Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences.
Engineering 5.5 · Research 7.0 · Business 5.0
This paper presents an autonomous driving algorithm engineered and executed using Proximal Policy Optimization (PPO), a reinforcement learning (RL) technique, within the Car Learning to Act (CARLA) simulation environment.
Engineering 5.0 · Research 7.0 · Business 5.0
To this end, we redirect the focus from accuracy only to both accuracy and efficiency.
Engineering 5.5 · Research 8.0 · Business 5.0
To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke.
Engineering 5.5 · Research 8.0 · Business 5.5