Integrating Shared Information into the Sensorial Mapping of Connected and Autonomous Vehicles

Filipo Perotto, Stephanie Combettes, Valerie Camps, Elsy Kaddoum, Guilhem Marcillaud, Pierre Glize, Marie-Pierre Gleizes

Abstract

A connected and autonomous vehicle (CAV) needs to dynamically maintain a map of its environment. Even if the self-positioning and relative localization of static objects (roads, signs, poles, guard-rails, buildings, etc.) can be done with great precision thanks to the help of hd-maps, the detection of the dynamic objects on the scene (other vehicles, bicycles, pedestrians, animals, casual objects, etc.) must be made by the CAV itself based on the interpretation of its low-level sensors (radars, lidars, cameras, etc.). In addition to the need of representing those moving objects around it, the CAV (seen as an agent immersed in that traffic environment) must identify them and understand their behavior in order to anticipate their expected trajectories. The accuracy and completeness of this real-time map, necessary for safely planning its own maneuvers, can be improved by incorporating the information transmitted by other vehicles or entities within the surrounding neighborhood through V2X communications. The implementation of this cooperative perception can be seen as the last phase of perception fusion, after the in-vehicle signals (coming from its diverse sensors) have already been combined. In this position paper, we approach the problem of creating a coherent map of objects by selecting relevant information sent by the neighbor agents. This task requires correctly identifying the position of other communicant agents, based both on the own sensory perception and on the received information, and then correcting and completing the map of perceived objects with the communicated ones. For doing so, the precision and confidence on each information must be taken into account, as well as the trust and latency associated with each source. The broad objective is to model and simulate a fleet of vehicles with different levels of autonomy and cooperation, based on a multi-agent architecture, in order to study and improve road safety, traffic efficiency, and passenger comfort. In the paper, the problem is stated, a brief survey of the state-of-the-art on related topics is given, and the sketch of a solution is proposed.

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Paper Citation


in Harvard Style

Perotto F., Combettes S., Camps V., Kaddoum E., Marcillaud G., Glize P. and Gleizes M. (2021). Integrating Shared Information into the Sensorial Mapping of Connected and Autonomous Vehicles.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-484-8, pages 454-461. DOI: 10.5220/0010387604540461


in Bibtex Style

@conference{icaart21,
author={Filipo Perotto and Stephanie Combettes and Valerie Camps and Elsy Kaddoum and Guilhem Marcillaud and Pierre Glize and Marie-Pierre Gleizes},
title={Integrating Shared Information into the Sensorial Mapping of Connected and Autonomous Vehicles},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2021},
pages={454-461},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010387604540461},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Integrating Shared Information into the Sensorial Mapping of Connected and Autonomous Vehicles
SN - 978-989-758-484-8
AU - Perotto F.
AU - Combettes S.
AU - Camps V.
AU - Kaddoum E.
AU - Marcillaud G.
AU - Glize P.
AU - Gleizes M.
PY - 2021
SP - 454
EP - 461
DO - 10.5220/0010387604540461