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Sergio Fernandez, Technical Lead, Valeo, Germany

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Sergio Fernandez, Technical Lead, Valeo, Germany

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Come and contribute to this exciting workshop packed with expert knowledge from industrial and academic leaders!

Tailored to promote interactions, combining excellent talks, an interactive tutorial with group activities, panel and poster sessions.

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Session 1 (Intro):

• Introduction and Tutorial Too much noise about…automotive sensors, Prof Donzella, University of Warwick

• Poster Session and Interactive word-cloud

Session 2 (Talks):

• Invited-talk: "Denoising is all we need for robust 3D perception in adverse weather", Prof Eren Aksoy, Halmstad-University, Sweden;

• Invited-talk: “The Impact of Rain on Lidar Detection Performance in Real-World Autonomous Driving”, Joonwoo Son, Sonnet.ai, South Korea;

• Panel Q&A;

Session 3 (Activities) :

• Instructions

• Group-activity

• Swapping groups

• Group presentations/wrap up

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Prof Eren Aksoy, Associate Professor, Halmstad University, Sweden

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Prof Eren Aksoy, Associate Professor, Halmstad University, Sweden

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Joonwoo Son, Founder & Chair, Sonnet.ai, South Korea

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Joonwoo Son, Founder & Chair, Sonnet.ai, South Korea

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Submit your abstract for the poster session HERE:

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Submit your abstract for the poster session HERE:

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IV Sensors (warwick.ac.uk)

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"Targeting Level-4 with LiDAR: reaching robustness against environmental artifacts with dense point cloud"

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"Targeting Level-4 with LiDAR: reaching robustness against environmental artifacts with dense point cloud"

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In order to meet L4, the regulation requires that the vehicle is able to detect any object that could jeopardize the safety of the vehicle from a distance of 150m (180m detection to have time to confirm the object presence and decide on action to be taken). This can happen in a wide variety of environmental conditions. In the case of LiDAR based perception algorithms, environmental disturbances, specially humidity artifacts, have a negative impact in the infrared detection bandwidth where LiDAR sends and receives the signal. In this talk, I will present several robustness approaches (based on physical and learning methods) to identify and mitigate the effects of environmental conditions in LiDAR dense point clouds. Also, a novel concept usage of LiDAR and HD radar data for better robustness coverage of ODD scenarios will be presented.

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Adverse conditions captured by different perception sensors, top camera, middle thermal, bottom LiDAR

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“The Impact of Rain on Lidar Detection Performance in Real-World Autonomous Driving”

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“The Impact of Rain on Lidar Detection Performance in Real-World Autonomous Driving”

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The rapidly ageing and depopulating cities in Korea, Japan, and Europe are increasing the demand for autonomous public transportation services. In order to provide transportation services 365 days a year, autonomous driving must be able to operate smoothly under adverse weather conditions such as rain, fog, and snowfall.
In this talk, I will share our experience with self-driving robotaxi services that have been operating regularly since 2021, and the impact of rainy weather on the performance of lidar sensors.

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Date: 20th October 2024, Kobe, Japan

Autonomous and Automated Vehicles are on the verge of modifying completely travelling, commuting, and how different countries approach transportation modalities. This transportation revolution is accompanied by the promise of safer, more sustainable and inclusive journeys. With the increased importance of different perception sensor technologies, this workshop brings together sensors and automotive experts to explore the main challenges of sensing in the multifaceted, continuously changing, and unpredictable driving environment. The workshop starts a thought-provoking initial tutorial, combined with some high-profile invited talks/posters presented by world-renowned professionals from industry and academia. This structure leads to a final interactive group-work/discussion to promote idea-exchange and capture key aspects of sensors’ noise factors, data quality evaluation, data processing/fusion, challenging the ‘status quo’ and providing future directions to the wider sensors and automotive communities.

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IEEE SENSORS 2024 is a sensors and sensing technologies forum for research scientists, engineers, and practitioners held in Kobe, Japan.

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Poster Topic:

Robust perception sensing for assisted, automated and autonomous vehicles and future transportation.

Submission Link:

Please submit your abstract (500 words) relating to the poster topic in the link.

https://forms.office.com/e/zkhiWp39GN

Submission Deadline:

15:00(GMT) 26th September, 2024

Notification Date:

30th September, 2024

Poster requirements:

Upon acceptance, authors are expected to bring their own poster to the workshop in the form of an A0 portrait poster.

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"Denoising is all we need for robust 3D perception in adverse weather"

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"Denoising is all we need for robust 3D perception in adverse weather"

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In this talk, I will present our EU-funded Horizon Europe Innovation Action project ROADVIEW. The project aims to develop robust and cost-effective in-vehicle perception and decision-making systems that can operate under extreme weather conditions such as fog, rain, and snow. After a brief overview of the project, I will present our results related to (un)supervised cleaning of snowy LiDAR point clouds, annotated multimodal sensor data recorded under harsh weather conditions, sensor fusion for slipperiness detection, and weather-conditional minimal risk maneuvering mechanism.

https://roadview-project.eu