IoT Technologies for Connected and Automated Driving Applications

The applications of the Internet of Things (IoT) technologies connect multiple devices directly and through the Internet. Autonomous vehicles utilise connectivity when updating their algorithms based on user data, interact with the infrastructure to get environmental information, communicate with other vehicles. They exchange information with pedestrians using mobile devices and wearables and provide information about the traffic attributes and data collected by the vehicle sensors. The connected and automated vehicles (CAV) require a significant quantity of collecting and processing data and through IoT applications and services the autonomous vehicles share information about the road, the present path, traffic, and how to navigate around different obstacles. This information can be shared between IoT connected vehicles and uploaded wirelessly to the cloud or/and edge system to be analysed and operated improving the levels of automation and the autonomous driving (AD) functions of each vehicle. This chapter gives an overview of the integration of IoT devices contributing to automated/autonomous driving, and the IoT infrastructure deployed and seamlessly integrated into the AUTOPILOT project use cases and pilot demonstrators, including the IoT platforms integration

Design and construction of a 38 t resistive magnet at the nijmegen high field magnet laboratory

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AUTOPILOT Deliverable 5.8: Standards and conformance of IoT in AD

This document reports the activities carried out to contribute to standardisation of IoT in the context of mobility and automated driving as well as the project activities relating to IoT platform interoperability testing, i.e. TESTFEST in AUTOPILOT project

Learn from IoT:pedestrian detection and intention prediction for autonomous driving

This paper explores the potential of machine learning (ML) systems which use data from in-vehicle sensors as well as external IoT data sources to enhance autonomous driving for efficiency and safety in urban environments. We propose a system which combines sensor data from autonomous vehicles and IoT data collected from pedestrians' mobile devices. Our approach includes two methods for vulnerable road user (VRU) detection and pedestrian movement intention prediction, and a model to combine the two outputs for potentially improving the autonomous decision-making. The first method creates a world model (WM) and accurately localizes VRUs using in-vehicle cameras and external mobile device data. The second method is a deep learning model to predict pedestrian's next movement steps using real-time trajectory and training with historical mobile device data. To test the system, we conduct three pilot tests at a university campus with a custom-built autonomous car and mobile devices carried by pedestrians. The results from our controlled experiments show that VRU detection can more accurately distinguish locations of pedestrians using IoT data. Furthermore, up to five future steps of pedestrians can be predicted within 2 m

Learn from IoT: pedestrian detection and intention prediction for autonomous driving

This paper explores the potential of machine learning (ML) systems which use data from in-vehicle sensors as well as external IoT data sources to enhance autonomous driving for efficiency and safety in urban environments. We propose a system which combines sensor data from autonomous vehicles and IoT data collected from pedestrians' mobile devices. Our approach includes two methods for vulnerable road user (VRU) detection and pedestrian movement intention prediction, and a model to combine the two outputs for potentially improving the autonomous decision-making. The first method creates a world model (WM) and accurately localizes VRUs using in-vehicle cameras and external mobile device data. The second method is a deep learning model to predict pedestrian's next movement steps using real-time trajectory and training with historical mobile device data. To test the system, we conduct three pilot tests at a university campus with a custom-built autonomous car and mobile devices carried by pedestrians. The results from our controlled experiments show that VRU detection can more accurately distinguish locations of pedestrians using IoT data. Furthermore, up to five future steps of pedestrians can be predicted within 2 m

Cooperative automated driving for various traffic scenarios: experimental validation in the GCDC 2016

Cooperative automated driving is a promising technology to improve road safety, fuel consumption, and traffic throughput without the need to expand the current infrastructure. To accelerate the developments in cooperative driving toward deployment in realistic traffic, the second grand cooperative driving challenge (GCDC) took place in Helmond, The Netherlands, in 2016. The aim of this implementation oriented challenge is to validate the practical feasibility and benefits of cooperative automated driving in the context of several advanced traffic scenarios, including cooperative merging on a highway and cooperative intersection crossing. Since all scenarios require road participants to cooperate, an interaction protocol was provided for each scenario. Except for this pre-defined interaction protocol, each team had full flexibility in developing the cooperative automated vehicle system. As such, one of the main difficulties of the challenge was to ensure interoperability despite the fact that each road participant might use different vehicle types and vehicle control systems that have been developed independently. In this paper, we provide an overview of the ATeam's implementation of the cooperative automated driving system for the cooperative merging on a highway and cooperative intersection crossing scenarios. This overview addresses the hardware architecture used during GCDC and, the design and integration of the required software layers. This paper also addresses practical issues that need to be taken into account when developing a cooperative automated driving system such as the limited capabilities of sensors and imperfections induced by the packet-based communication. Moreover, we present experimental results that were obtained during the challenge

Cooperative automated driving for various traffic scenarios:experimental validation in the GCDC 2016

\u3cp\u3eCooperative automated driving is a promising technology to improve road safety, fuel consumption, and traffic throughput without the need to expand the current infrastructure. To accelerate the developments in cooperative driving toward deployment in realistic traffic, the second grand cooperative driving challenge (GCDC) took place in Helmond, The Netherlands, in 2016. The aim of this implementation oriented challenge is to validate the practical feasibility and benefits of cooperative automated driving in the context of several advanced traffic scenarios, including cooperative merging on a highway and cooperative intersection crossing. Since all scenarios require road participants to cooperate, an interaction protocol was provided for each scenario. Except for this pre-defined interaction protocol, each team had full flexibility in developing the cooperative automated vehicle system. As such, one of the main difficulties of the challenge was to ensure interoperability despite the fact that each road participant might use different vehicle types and vehicle control systems that have been developed independently. In this paper, we provide an overview of the ATeam's implementation of the cooperative automated driving system for the cooperative merging on a highway and cooperative intersection crossing scenarios. This overview addresses the hardware architecture used during GCDC and, the design and integration of the required software layers. This paper also addresses practical issues that need to be taken into account when developing a cooperative automated driving system such as the limited capabilities of sensors and imperfections induced by the packet-based communication. Moreover, we present experimental results that were obtained during the challenge.\u3c/p\u3