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.
With artificial intelligence technology progressing at a tremendous speed, intelligent driving has got a lot of recognition in recent years.
Engineering 5.0 · Research 7.0 · Business 5.0
We present nuPlan, the world’s first real-world autonomous driving dataset and benchmark.
Engineering 6.0 · Research 7.0 · Business 5.5
Currently, real-time detection networks elaborate the technical details of the Faster Regional Convolution Neural Network (R-CNN) recognition pipeline.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper presents a novel multi-modal Multi-Object Tracking (MOT) algorithm for self-driving cars that combines camera and LiDAR data.
Engineering 5.5 · Research 8.0 · Business 5.5
In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficiency, critically aiding the decision-making process.
Engineering 5.0 · Research 7.0 · Business 5.0
To this end, a new method, dubbed FastOcc, is proposed.
Engineering 5.5 · Research 8.0 · Business 5.0
3D object detection based on LiDAR point cloud and prior anchor boxes is a critical technology for autonomous driving environment perception and understanding.
Engineering 6.5 · Research 8.0 · Business 5.0
To improve the online collaboration and planning capabilities between autonomous vehicles, this paper proposes a novel collaborative motion planning method.
Engineering 5.0 · Research 8.0 · Business 5.0
This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature.
Engineering 5.0 · Research 7.0 · Business 5.0
In autonomous vehicle (AV) technology, the ability to accurately predict the movements of surrounding vehicles is paramount for ensuring safety and operational efficiency.
Engineering 5.0 · Research 7.0 · Business 5.0
Through rigorous validation, our method demonstrates marked improvements over traditional models, establishing a new benchmark for safety and operational efficiency in intelligent transportation systems.
Engineering 5.0 · Research 8.0 · Business 5.0
Our approach relies solely on visual perception as the input to generate control commands.
Engineering 5.5 · Research 8.0 · Business 5.0
Autonomous driving (AD) technology, leveraging artificial intelligence, strives for vehicle automation.
Engineering 5.5 · Research 8.0 · Business 5.0
In this work, we propose a probabilistic planning model for end-to-end autonomous driving, termed VADv2.
Engineering 7.5 · Research 8.0 · Business 5.5
We introduce DriveVLM, an autonomous driving system leveraging Vision-Language Models (VLMs) for enhanced scene understanding and planning capabilities.
Engineering 6.0 · Research 7.0 · Business 6.5
In this paper, we explore a new paradigm for end-to-end autonomous driving, where the key is to predict how the ego car and the surroundings evolve given past scenes.
Engineering 7.5 · Research 8.0 · Business 5.0
In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance.
Engineering 5.0 · Research 7.0 · Business 5.0
Extensive testing is necessary to ensure the safety of autonomous driving modules.
Engineering 6.5 · Research 8.0 · Business 5.0
We present MoNA Bench, a benchmark for Monocular depth estimation in Navigation of the Autonomous unmanned Aircraft system (MoNA), emphasizing its obstacle avoidance and safe target tracking capabilities.
Engineering 5.0 · Research 8.0 · Business 5.0
The paper presents a technique of motion planning for autonomous vehicles (AV) based on simultaneous trajectory and speed optimization.
Engineering 5.0 · Research 7.0 · Business 5.0