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You Li

Publications
21
Citations
731
Publications by Year
20112023
Co-Authors:
Show All Co-Authors (22)
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You Li

Affiliation

Embotech AG, Zurich, Switzerland

Publication Topics

Biography

You Li received the B.Sc. degree from the Nanjing University of Science and Technology, Nanjing, China, in 2006, the M.Sc. degree from Shanghai Jiao Tong University, Shanghai, China, in 2010, and the Ph.D. degree in machine perception applied to intelligent vehicles from the Universite de Technologie de Belfort Montbliard, Belfort, France, in 2013. He is a Senior Research Engineer with the Groupe RENAULT. From 2014, he worked as a Postdoctoral Researcher with the CNRS (French National Center for Scientific Research) on unmanned surface vehicle. In 2016, he joined TNO (Netherlands Organization for Applied Scientific Research) as a Research Scientist working on sensor fusion for autonomous vehicles. Since 2017, he is with research division of...
Publications
21
Citations
731
Publications by Year
20112023
Co-Authors:
Show All Co-Authors (22)
Author's Published Works

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Learn to Model and Filter Point Cloud Noise for a Near-Infrared ToF LiDAR in Adverse Weather

Tao Yang;Qiyan Yu;You Li;Zhi Yan

IEEE Sensors Journal
Year: 2023 | Volume: 23, Issue: 17 | Journal Article |
Cited by: Papers (7)
Light detection and ranging (LiDAR) limitations in adverse weather (e.g., rain, fog, and snow) prevent adopting high-level autonomous vehicles in all weather conditions. Furthermore, collecting and annotating these sparse point clouds in adverse weather is often cumbersome, inefficient, and expensive. In this article, we propose a data-driven approach to statistically model the performance of a po...Show More

Learn to Model and Filter Point Cloud Noise for a Near-Infrared ToF LiDAR in Adverse Weather

Tao Yang;Qiyan Yu;You Li;Zhi Yan

Year: 2023 | Volume: 23, Issue: 17 | Journal Article |

Emergent Visual Sensors for Autonomous Vehicles

You Li;Julien Moreau;Javier Ibanez-Guzman

IEEE Transactions on Intelligent Transportation Systems
Year: 2023 | Volume: 24, Issue: 5 | Journal Article |
Cited by: Papers (22)
For vehicles to navigate autonomously, they need to perceive and understand their immediate surroundings. Currently, cameras are the preferred sensors, due to their high performance and relatively low-cost compared with other sensors like LiDARs and Radars. However, their performance is limited by inherent imaging constraints, a standard RGB camera may perform poorly in extreme conditions, includi...Show More

Emergent Visual Sensors for Autonomous Vehicles

You Li;Julien Moreau;Javier Ibanez-Guzman

Year: 2023 | Volume: 24, Issue: 5 | Journal Article |

Spatial-Temporal Measurement Alignment of Rolling Shutter Camera and LiDAR

You Li

IEEE Sensors Letters
Year: 2022 | Volume: 6, Issue: 12 | Journal Article |
This letter proposes a novel method to fuse the asynchronized outputs from a rolling shutter camera and a spinning LiDAR mounted on a moving vehicle. Compared with traditional methods only relying on intrinsic/extrinsic calibration, the proposed method takes the ego-motion, rolling shutter distortion, and occlusion into the fusion model. In essence, the method estimates the temporal offset between...Show More

Spatial-Temporal Measurement Alignment of Rolling Shutter Camera and LiDAR

You Li

Year: 2022 | Volume: 6, Issue: 12 | Journal Article |

Performance Modeling a Near-Infrared ToF LiDAR Under Fog: A Data-Driven Approach

Tao Yang;You Li;Yassine Ruichek;Zhi Yan

IEEE Transactions on Intelligent Transportation Systems
Year: 2022 | Volume: 23, Issue: 8 | Journal Article |
Cited by: Papers (19)
As a critical sensor for high-level autonomous vehicles, LiDAR’s limitations in adverse weather (e.g. rain, fog, snow, etc.) impede the deployment of self-driving cars in all weather conditions. However, studies in literature on LiDAR’s performance in harsh conditions are insufficient. In this paper, based on a dataset collected with a popular Near-InfraRed (NIR) ToF LiDAR in a well-controlled art...Show More

Performance Modeling a Near-Infrared ToF LiDAR Under Fog: A Data-Driven Approach

Tao Yang;You Li;Yassine Ruichek;Zhi Yan

Year: 2022 | Volume: 23, Issue: 8 | Journal Article |

Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning

Abdelhak Loukkal;Yves Grandvalet;Tom Drummond;You Li

2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
Year: 2021 | Conference Paper |
Cited by: Papers (17)
Camera-based end-to-end driving neural networks bring the promise of a low-cost system that maps camera images to driving control commands. These networks are appealing because they replace laborious hand engineered building blocks but their black-box nature makes them difficult to delve in case of failure. Recent works have shown the importance of using an explicit intermediate representation tha...Show More

Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning

Abdelhak Loukkal;Yves Grandvalet;Tom Drummond;You Li

Year: 2021 | Conference Paper |

What Happens for a ToF LiDAR in Fog?

You Li;Pierre Duthon;Michèle Colomb;Javier Ibanez-Guzman

IEEE Transactions on Intelligent Transportation Systems
Year: 2021 | Volume: 22, Issue: 11 | Journal Article |
Cited by: Papers (77)
By transmitting lasers and processing laser returns, LiDAR (light detection and ranging) perceives the surrounding environment through distance measurements. Because of high ranging accuracy, LiDAR is one of the most critical sensors in autonomous driving systems. Revolving around the 3D point clouds generated from LiDARs, plentiful algorithms have been developed for object detection/tracking, env...Show More

What Happens for a ToF LiDAR in Fog?

You Li;Pierre Duthon;Michèle Colomb;Javier Ibanez-Guzman

Year: 2021 | Volume: 22, Issue: 11 | Journal Article |

LaNoising: A Data-driven Approach for 903nm ToF LiDAR Performance Modeling under Fog

Tao Yang;You Li;Yassine Ruichek;Zhi Yan

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2020 | Conference Paper |
Cited by: Papers (17)
As a critical sensor for high-level autonomous vehicles, LiDAR's limitations in adverse weather (e.g. rain, fog, snow, etc.) impede the deployment of self-driving cars in all weather conditions. In this paper, we model the performance of a popular 903nm ToF LiDAR under various fog conditions based on a LiDAR dataset collected in a well-controlled artificial fog chamber. Specifically, a two-stage d...Show More

LaNoising: A Data-driven Approach for 903nm ToF LiDAR Performance Modeling under Fog

Tao Yang;You Li;Yassine Ruichek;Zhi Yan

Year: 2020 | Conference Paper |

InsClustering: Instantly Clustering LiDAR Range Measures for Autonomous Vehicle

You Li;Clément Le Bihan;Txomin Pourtau;Thomas Ristorcelli

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Year: 2020 | Conference Paper |
Cited by: Papers (7)
LiDARs are usually more accurate than cameras in distance measuring. Hence, there is strong interest to apply LiDARs in autonomous driving. Different existing approaches process the rich 3D point clouds for object detection, tracking and recognition. These methods generally require two initial steps: (1) filter points on the ground plane and (2) cluster non-ground points into objects. This paper p...Show More

InsClustering: Instantly Clustering LiDAR Range Measures for Autonomous Vehicle

You Li;Clément Le Bihan;Txomin Pourtau;Thomas Ristorcelli

Year: 2020 | Conference Paper |

Coarse-to-Fine Segmentation on LiDAR Point Clouds in Spherical Coordinate and Beyond

You Li;Clément Le Bihan;Txomin Pourtau;Thomas Ristorcelli;Javier Ibanez-Guzman

IEEE Transactions on Vehicular Technology
Year: 2020 | Volume: 69, Issue: 12 | Journal Article |
Cited by: Papers (9)
Active sensors such as LiDARs (light detection and ranging) are popular in autonomous driving systems for perception and localization. Existing perception approaches process the rich 3D LiDAR point clouds for object detection, tracking and recognition. These methods generally require an initial segmentation procedure containing two steps: (1) filter points as ground and non-ground points, and (2) ...Show More

Coarse-to-Fine Segmentation on LiDAR Point Clouds in Spherical Coordinate and Beyond

You Li;Clément Le Bihan;Txomin Pourtau;Thomas Ristorcelli;Javier Ibanez-Guzman

Year: 2020 | Volume: 69, Issue: 12 | Journal Article |

Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems

You Li;Javier Ibanez-Guzman

IEEE Signal Processing Magazine
Year: 2020 | Volume: 37, Issue: 4 | Magazine Article |
Cited by: Papers (419)
Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians, and other relevant entities. Safety concerns and the need for accurate estimations have led to the introduction of lidar systems to complement camera- or radar-based perception systems. This article presents a review ...Show More

Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems

You Li;Javier Ibanez-Guzman

Year: 2020 | Volume: 37, Issue: 4 | Magazine Article |

Disparity weighted loss for semantic segmentation of driving scenes

Abdelhak Loukkal;Yves Grandvalet;You Li

2019 IEEE Intelligent Transportation Systems Conference (ITSC)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
Convolutional neural networks are the state of the art methods for semantic segmentation but their resource consumption hinders their usability for real-time mobile robotics applications. Recent works have focused on designing lightweight networks that require less resources, but their efficiency is accompanied with a drop in performance. In this work, we propose a pixel-wise weighting of the cros...Show More

Disparity weighted loss for semantic segmentation of driving scenes

Abdelhak Loukkal;Yves Grandvalet;You Li

Year: 2019 | Conference Paper |

Evidential deep learning for arbitrary LIDAR object classification in the context of autonomous driving

Edouard Capellier;Franck Davoine;Veronique Cherfaoui;You Li

2019 IEEE Intelligent Vehicles Symposium (IV)
Year: 2019 | Conference Paper |
Cited by: Papers (22)
In traditional LIDAR processing pipelines, a point-cloud is split into clusters, or objects, which are classified afterwards. This supposes that all the objects obtained by clustering belong to one of the classes that the classifier can recognize, which is hard to guarantee in practice. We thus propose an evidential end-to-end deep neural network to classify LIDAR objects. The system is capable of...Show More

Evidential deep learning for arbitrary LIDAR object classification in the context of autonomous driving

Edouard Capellier;Franck Davoine;Veronique Cherfaoui;You Li

Year: 2019 | Conference Paper |

Improving semantic segmentation in urban scenes with a cartographic information

Abdelhak Loukkal;Vincent Fremont;Yves Grandvalet;You Li

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
This paper presents three different approaches to inject a location information in semantic segmentation Convolutional Neural Networks (CNN) applied to urban scenes. The assumption that a location information would improve semantic segmentation performance emerges from the idea that some elements of urban scenes are located in a predictable manner. This assumption is confronted to realistic data o...Show More

Improving semantic segmentation in urban scenes with a cartographic information

Abdelhak Loukkal;Vincent Fremont;Yves Grandvalet;You Li

Year: 2018 | Conference Paper |

Evidential grid mapping, from asynchronous LIDAR scans and RGB images, for autonomous driving

Edouard Capellier;Franck Davoine;Vincent Fremont;Javier Ibanez-Guzman;You Li

2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Year: 2018 | Conference Paper |
Cited by: Papers (10)
We propose an evidential fusion algorithm between LIDAR scans and RGB images. LIDAR points are classified as either belonging to the ground, or not, and RGB images are processed by a state-of-the-art convolutional neural network to obtain semantic labels. The results are fused into an evidential grid to assess the drivability of an area met by an autonomous vehicle, while accounting for incoherenc...Show More

Evidential grid mapping, from asynchronous LIDAR scans and RGB images, for autonomous driving

Edouard Capellier;Franck Davoine;Vincent Fremont;Javier Ibanez-Guzman;You Li

Year: 2018 | Conference Paper |

Concept alignment of product taxonomies based on semantic similarity

Weimin Jiang;Yumin Lin;You Li

2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)
Year: 2018 | Conference Paper |
Cited by: Papers (1)
The product taxonomy can provide knowledge support for many knowledge-based Web services. Merging different taxonomies is an effective way to build a complete product taxonomy. To achieve this goal, a core task is to align the concepts of different taxonomies. The traditional methods of conceptual alignment are insufficient in accuracy and efficiency. In this paper, we propose a new concept alignm...Show More

Concept alignment of product taxonomies based on semantic similarity

Weimin Jiang;Yumin Lin;You Li

Year: 2018 | Conference Paper |

Multiframe-Based High Dynamic Range Monocular Vision System for Advanced Driver Assistance Systems

You Li;Yongliang Qiao;Yassine Ruichek

IEEE Sensors Journal
Year: 2015 | Volume: 15, Issue: 10 | Journal Article |
Cited by: Papers (23)
In this paper, we propose a monocular multiframe high dynamic range (HDR) monocular vision system to improve the imaging quality of traditional CMOS/charge-coupled device (CCD)-based vision system for advanced driver assistance systems (ADASs). Conventional CMOS/CCD image sensors are confined to limited dynamic range that it impairs the imaging quality under undesirable environments for ADAS (e.g....Show More

Multiframe-Based High Dynamic Range Monocular Vision System for Advanced Driver Assistance Systems

You Li;Yongliang Qiao;Yassine Ruichek

Year: 2015 | Volume: 15, Issue: 10 | Journal Article |

Building variable resolution occupancy grid map from stereoscopic system — A quadtree based approach

You Li;Yassine Ruichek

2013 IEEE Intelligent Vehicles Symposium (IV)
Year: 2013 | Conference Paper |
Cited by: Papers (6) | Patents (2)
In intelligent vehicle field, occupancy grid maps are popular tools for representing the environment. Usually, occupancy grids, mapping the environment as a field of uniformly distributed binary/ternary variables, are generated by various kinds of sensors (e.g. lidar, radar, monocular/binocular vision system). In literature, most of proposed occupancy grid mapping methods create array-based fixed-...Show More

Building variable resolution occupancy grid map from stereoscopic system — A quadtree based approach

You Li;Yassine Ruichek

Year: 2013 | Conference Paper |

Optimal Extrinsic Calibration Between a Stereoscopic System and a LIDAR

You Li;Yassine Ruichek;Cindy Cappelle

IEEE Transactions on Instrumentation and Measurement
Year: 2013 | Volume: 62, Issue: 8 | Journal Article |
Cited by: Papers (45)
Current perception systems of intelligent vehicles not only make use of visual sensors, but also take advantage of depth sensors. Extrinsic calibration of these heterogeneous sensors is required for fusing information obtained separately by vision sensors and light detection and ranging (LIDARs). In this paper, an optimal extrinsic calibration algorithm between a binocular stereo vision system and...Show More

Optimal Extrinsic Calibration Between a Stereoscopic System and a LIDAR

You Li;Yassine Ruichek;Cindy Cappelle

Year: 2013 | Volume: 62, Issue: 8 | Journal Article |

Extrinsic calibration between a stereoscopic system and a LIDAR with sensor noise models

You Li;Yassine Ruichek;Cindy Cappelle

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
Year: 2012 | Conference Paper |
Cited by: Papers (8) | Patents (1)
Visual sensors and depth sensors, such as camera and LIDAR (Light Detection and Ranging) are more and more used together in current perception systems of intelligent vehicles. Fusing information obtained separately from these heterogeneous sensors always requires extrinsic calibration of vision sensors and LIDARs. In this paper, we propose an optimal extrinsic calibration algorithm between a binoc...Show More

Extrinsic calibration between a stereoscopic system and a LIDAR with sensor noise models

You Li;Yassine Ruichek;Cindy Cappelle

Year: 2012 | Conference Paper |

Moving objects detection and recognition using sparse spatial information in urban environments

You Li;Yassine Ruichek

2012 IEEE Intelligent Vehicles Symposium
Year: 2012 | Conference Paper |
Cited by: Papers (2)
Moving objects detection and recognition around an intelligent vehicle are active research fields. A great number of approaches have been proposed in recent decades. This paper proposes a novel approach based solely on spatial information to solve this problem. Moving objects detection is achieved in conjunction with an egomotion estimation by sparse matched feature points. For objects recognition...Show More

Moving objects detection and recognition using sparse spatial information in urban environments

You Li;Yassine Ruichek

Year: 2012 | Conference Paper |

3D triangulation based extrinsic calibration between a stereo vision system and a LIDAR

You Li;Yassine Ruichek;Cindy Cappelle

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Year: 2011 | Conference Paper |
Cited by: Papers (17)
This paper presents a novel extrinsic calibration algorithm between a binocular stereo vision system and a 2D LIDAR (laser range finder). Extrinsic calibration of these heterogeneous sensors is required to fuse information obtained separately by vision sensor and LIDAR in the context of intelligent vehicle. By placing a planar chessboard at different positions and orientations in front of the sens...Show More

3D triangulation based extrinsic calibration between a stereo vision system and a LIDAR

You Li;Yassine Ruichek;Cindy Cappelle

Year: 2011 | Conference Paper |

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