Infrastructure based communication architecture to facilitate autonomous driving and communications
Rajapakshalage, Dhanushka (2019-09-20)
Rajapakshalage, Dhanushka
D. Rajapakshalage
20.09.2019
© 2019 Dhanushka Rajapakshalage. Tämä Kohde on tekijänoikeuden ja/tai lähioikeuksien suojaama. Voit käyttää Kohdetta käyttöösi sovellettavan tekijänoikeutta ja lähioikeuksia koskevan lainsäädännön sallimilla tavoilla. Muunlaista käyttöä varten tarvitset oikeudenhaltijoiden luvan.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-201909242926
https://urn.fi/URN:NBN:fi:oulu-201909242926
Tiivistelmä
The traditional autonomous vehicle (AV) architecture places a heavy burden on graphics processing units of the vehicle due to heavy signal processing requirements. Ultimately this results in performance degradation in AVs. This is mainly due to advanced sensors, which enable the vision for AVs, like Light Detection and Ranging (LiDAR), radars and cameras. In most of the AV models accepted by many leading automobile companies, LiDAR plays a significant role. It generates a high definition (HD) point cloud of the surroundings to obtain a precise map. AV makes decisions based on that by processing Terabyte (Tb) scale data within the AV. Still, vehicle-mounted LiDARs are not capable of providing information beyond a human driver’s vision.
To provide a solution for the above-mentioned drawbacks of the traditional AVs, we propose an infrastructure based communication architecture to facilitate autonomous driving and communications. A set of coordinated LiDAR modules with integrated transceivers, which are mounted at an elevation with a bird’s eye view, can provide a much larger field of vision (FoV). Decisions are taken from a centralized body. We prove the technical feasibility of the system from sensing and communication point of view. The proposed architecture can play a supportive role with traditional AV architectures and it can be applied to many cases such as to automate harbours and factory floors.
In the second part of the thesis, we address a resource allocation problem with ultra-reliable and low latency communication (URLLC) for a factory floor. We have analytically proven the capability of the proposed system to establish a reliable (packet error probability less than 10^(-5)) and low latency (less than 1 ms transmission delay) links with sufficient throughput (kilobit scale) using a convex optimization problem. Latency, throughput and reliability variations are studied under the short packet transmission of the proposed system.
To provide a solution for the above-mentioned drawbacks of the traditional AVs, we propose an infrastructure based communication architecture to facilitate autonomous driving and communications. A set of coordinated LiDAR modules with integrated transceivers, which are mounted at an elevation with a bird’s eye view, can provide a much larger field of vision (FoV). Decisions are taken from a centralized body. We prove the technical feasibility of the system from sensing and communication point of view. The proposed architecture can play a supportive role with traditional AV architectures and it can be applied to many cases such as to automate harbours and factory floors.
In the second part of the thesis, we address a resource allocation problem with ultra-reliable and low latency communication (URLLC) for a factory floor. We have analytically proven the capability of the proposed system to establish a reliable (packet error probability less than 10^(-5)) and low latency (less than 1 ms transmission delay) links with sufficient throughput (kilobit scale) using a convex optimization problem. Latency, throughput and reliability variations are studied under the short packet transmission of the proposed system.
Kokoelmat
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