i-CAVE
Date: 2017-01-01 to 2021-12-31
Type: NWO program
Website: http://www.stw.nl/nl/content/p14-18-i-cave-integrated-cooperative-automated-vehicle
Description:
i-CAVE (integrated Cooperative Automated VEhicles) examines all the key aspects of the self-driving car. The program’s participants will co-develop vehicles that are able to drive autonomously on closed roads and cooperatively on public roads. The researchers also focus thereby on the development of the requisite sensors like cameras and radar as well as the logistics involved and the human-car interaction. Participants in i-CAVE include companies like DAF, NXP and Ford as well as parties such as ANWB (the Dutch automobile association), TNO, AutomotiveNL and the Ministry of Infrastructure and the Environment.
AUTOPILOT
Date: 2017-01-01 to 2019-12-31
Type: H2020 Research and Innovation action
Website: http://autopilot-project.eu
Description:
Autopilot brings together relevant knowledge and technology from the automotive and the IoT value chains in order to develop IoT-architectures and platforms which will bring Automated Driving towards a new dimension. Its objectives are:
- Enhance the driving environment perception with IoT sensors enabling safer highly automated driving
- Foster innovation in automotive, IoT and mobility services
- Use and evaluate advanced vehicle-to-everything (V2X) connectivity technologies
- Involve Users, Public Services, Business Players to assess the IoT socio-economic benefits
- Contribute to the IoT Standardisation and eco-system
VI-DAS
Date: 2016-09-01 to 2019-08-31
Type: H2020 Research and Innovation action
Website: http://www.vi-das.eu/
Description:
Road accidents continue to be a major public safety concern. Human error is the main cause of accidents. Intelligent driver systems that can monitor the driver’s state and behaviour show promise for our collective safety. VI-DAS will progress the design of next-gen 720° connected ADAS (scene analysis, driver status). Advances in sensors, data fusion, machine learning and user feedback provide the capability to better understand driver, vehicle and scene context, facilitating a significant step along the road towards truly semi-autonomous vehicles. On this path there is a need to design vehicle automation that can gracefully hand-over and back to the driver. VI-DAS advances in computer vision and machine learning will introduce non-invasive, vision-based sensing capabilities to vehicles and enable contextual driver behaviour modelling.
INLANE
Date: 01-012016 to 31-12-2018
Type: H2020 Innovation action
Website: http://inlane.eu/
Description:
In short, the aim of the INLANE project is to 1) Deliver lane-level information to an in-vehicle navigation system giving drivers the opportunity to select the optimal road lane, even in the case of dense urban and extra-urban traffic. 2) reduce the risks associated with last-moment lane-change manoeuvres. 3) enable a new generation of enhanced mapping information based on crowd sourcing.
Cloud-LSVA
Date: 01-012016 to 31-12-2018
Type: H2020 Research and innovation action
Website: http://cloud-lsva.eu/
Description:
In short, the aim of this project is to develop a software platform for efficient and collaborative semiautomatic labelling and exploitation of large-scale video data solving existing needs for ADAS and Digital Cartography industries. Cloud-LSVA will use Big Data Technologies to address the open problem of a lack of software tools, and hardware platforms, to annotate petabyte scale video datasets, with the focus on the automotive industry. Annotations of road traffic objects, events and scenes are critical for training and testing computer vision techniques that are the heart of modern Advanced Driver Assistance Systems and Navigation systems. Providing this capability will establish a sustainable basis to drive forward automotive Big Data Technologies.
Change detection 2.0
Date: FINISHED 01-01-2015 to 31-12-2016
Type: DTP
Website: http://www.vinotion.nl
Description:
In short, the Change Detection (CD2.0) project focuses on exploiting 3D scene information in the context of Countering Improvised Explosive Devices (C-IED). This involves both the algorithmic design as well as a fully functional prototype vehicle. The algorithmic design encompasses a wide variety of computer vision tasks, such as scene alignment under different viewpoints, 3D scene segmentation and semantics, change detection, tracking from a moving vehicle etc.. We aim to have a fully operational prototype at the end of the project.
