Title

Self-Driving Vehicles: The Path Forward with LiDAR and V2X Technologies

Document Type

Conference Presentation

Publication Date

1-5-2017

Keywords

ITS, V2X, V2V, C-V2X, DSRC, VANET, 802.11p, 5G, LOS, WLOS, NLOS, CAV, URLLC, H2020, intelligent transportation system, connected vehicles, vehicle-to-everything, vehicle-to-vehicle, dedicated short-range communication, vehicular ad hoc network, path loss, propagation model, channel model, connected vehicle, autonomous vehicle, connected and autonomous vehicle, outage probability, reliability, ultra-reliable low-latency communication, stochastic geometry, meta distribution, road safety, traffic efficiency, urban intersection, suburban intersection, smart intersection, intelligent intersection, smart city, research, research funding, grant Proposals, government funding, European Commission, EU, MSCA, Marie-Curie, Marie Skłodowska Curie Action, Volvo, Ericsson

Abstract

Presentation delivered at IEEE VTS, ComSoc and SPS Seminar (2017). According to some estimates, most vehicles on the roads will have driverless capability by 2050. To achieve this level of “full autonomy” with no human intervention, the vehicles must reach a high degree of sophistication with artificial machine intelligence augmented with various light and radio detection, ranging, localization and communication technologies for uninterrupted remote sensing and coverage. And in this ecosystem, V2X capability is certainly an indispensable complementary mechanism to sensors/LiDAR for supplemental system redundancy, added security, and greater awareness of the vehicle’s surroundings. In this talk, we will explore the vision for driverless vehicles and highlight topics related to communications, traffic and control. Much emphasis will be given to V2X communications fidelity in urban and suburban environments based on empirically supported measurements. Ongoing research work with Chalmers and industrial partners in Sweden, such as Volvo and Ericsson, will be discussed.

Faculty

Faculty of Applied Science and Technology (FAST)

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Creative Commons License
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