The potential of collective perception in vehicular ad-hoc networks

Günther, Hendrik-Jörn; Trauer, Oliver; Wolf, Lars

doi:10.1109/itst.2015.7377190

Cited by 69 publications

(24 citation statements)

References 12 publications

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“…Cooperative Perception Messages (CPM) is specified in Ko-PER as a method for perceiving dynamic objects in the environment of an equipped vehicle or roadside station [17], [18]. Environmental Perception Message (EPM) is a proprietary message framework that includes a list of all perceived objects containing the unique IDs of all vehicles perceived by local perception sensors [19], [20]. Sensory Observation Message (SOM) has been proposed as a method for sharing infrastructure sensor information primarily for the purpose of defending vulnerable road users [21].…”

Section: Related Workmentioning

confidence: 99%

Cooperative awareness using roadside unit networks in mixed traffic

Tsukada

Kitazawa

et al. 2019

2019 IEEE Vehicular Networking Conference (VNC)

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Vehicle-to-vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems. Although V2V standards have been specified by the European Union, United States, and Japan, the deployment phase represents mixed traffic in which connected and legacy vehicles coexist. To enhance cooperative awareness in this mixed traffic, we assessed the special roadside unit that we developed in our previous work that generates required V2V messages on behalf of sensed target vehicles. In this paper, we extend our earlier work to propose a system called Grid Proxy Cooperative Awareness Message to broaden the cooperative awareness message dissemination area by connecting infrastructure using high-speed roadside networks. To minimize delay in message delivery, we designed the proposed system to use edge computing. The proposed scheme delivers cooperative messages to a wider area with a low delay and a high packet delivery ratio by prioritizing packets by their respective safety contributions. Our simulation results indicate that the proposed scheme efficiently delivers messages in heavy road traffic conditions modeled on real maps of Tokyo and Paris.

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Section: Related Workmentioning

confidence: 99%

Cooperative awareness using roadside unit networks in mixed traffic

Tsukada

Kitazawa

et al. 2019

2019 IEEE Vehicular Networking Conference (VNC)

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“…We start with a brief review of the local perception toolbox of Artery (Günther et al, 2015), which is shown in Fig. 3.…”

Section: Arterymentioning

confidence: 99%

“…the vehicle has not be sensed before by the ego vehicle, a new LEMO is created for that particular vehicle. In Günther et al (2015), there is one LEMO per sensor per perceived object. The LEMO stores the last measurements.…”

Section: Arterymentioning

confidence: 99%

Extending the vehicular network simulator Artery in order to generate synthetic data for collective perception

Allig

Wanielik

2019

Adv. Radio Sci.

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Abstract. A fundamental for an automated driving car is the awareness of all its surrounding road participants. Current approach to gather this awareness is to sense the environment by on-board sensors. In the future, Vehicle-to-X (V2X) might be able to improve the awareness due to V2X's communication range superiority compared to the on-board sensors' range. Due to a limited amount of communication partners sharing their own ego states, current research focuses particularly on cooperative perception. This means sharing objects perceived by local on-board sensors of different partners via V2X. Data collections using vehicles, driving on real roads, is challenging, since there is no market introduction of cooperative perception yet. Using test cars, equipped with the required sensors are rather expensive and do not necessarily provide results representing the true potential of cooperative perception. Particularly, its potential is highly dependent on the market penetration rate and the amount of vehicles within certain vicinity. Therefore, we consider to create synthetic data for cooperative perception by a simulation tool. After reviewing suitable simulation tools, we present an extension of Artery and its counterpart SUMO by modelling realistic vehicle dynamics and probabilistic sensor models. The generated data can be used as input for cooperative perception.

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“…In contrast, vehicles are equipped with a variety of equipment and can be therefore called sensors on wheels. In the study [2], the collective perception potential of connected vehicles is evaluated. Enhanced and Extended Floating Car Data (xFCD), where vehicle sensor probes are sent from the vehicle to a central server, have also been introduced and analyzed in a variety of studies [3], [4].…”

Section: Related Workmentioning

confidence: 99%

Novel common vehicle information model (CVIM) for future automotive vehicle big data marketplaces

Pillmann

Wietfeld

Zarcula

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Even though connectivity services have been introduced in many of the most recent car models, access to vehicle data is currently limited due to its proprietary nature. The European project AutoMat has therefore developed an open Marketplace providing a single point of access for brandindependent vehicle data. Thereby, vehicle sensor data can be leveraged for the design and implementation of entirely new services even beyond traffic-related applications (such as hyperlocal traffic forecasts). This paper presents the architecture for a Vehicle Big Data Marketplace as enabler of cross-sectorial and innovative vehicle data services. Therefore, the novel Common Vehicle Information Model (CVIM) is defined as an open and harmonized data model, allowing the aggregation of brandindependent and generic data sets. Within this work the realization of a prototype CVIM and Marketplace implementation is presented. The two use-cases of local weather prediction and road quality measurements are introduced to show the applicability of the AutoMat concept and prototype to nonautomotive applications.

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The potential of collective perception in vehicular ad-hoc networks

Cited by 69 publications

References 12 publications

Cooperative awareness using roadside unit networks in mixed traffic

Cooperative awareness using roadside unit networks in mixed traffic

Extending the vehicular network simulator Artery in order to generate synthetic data for collective perception

Novel common vehicle information model (CVIM) for future automotive vehicle big data marketplaces

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