Sökning: onr:"swepub:oai:research.chalmers.se:ceba15bc-db7f-457d-8028-5befcfe07cf3" >
Data-driven rolling...
Data-driven rolling eco-speed optimization for autonomous vehicles
-
- Yang, Ying (författare)
- Shanghai University
-
- Gao, Kun, 1993 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
-
Cui, Shaohua (författare)
-
visa fler...
-
- Xue, Yongjie (författare)
- Beihang University
-
- Najafi, Arsalan, 1987 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
-
- Andric, Jelena, 1979 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,Volvo Group
-
visa färre...
-
(creator_code:org_t)
- 2024
- 2024
- Engelska.
-
Ingår i: Frontiers of Engineering Management. - 2096-0255 .- 2095-7513. ; In Press
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://research.cha...
-
visa färre...
Abstract
Ämnesord
Stäng
- In urban settings, fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration, deceleration, and idling in vehicles. These maneuvers contribute to elevated energy use and emissions. Advances in vehicle-to-vehicle and vehicle-to-infrastructure communication technologies allow autonomous vehicles (AVs) to perceive signals over long distances and coordinate with other vehicles, thereby mitigating environmentally harmful maneuvers. This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation. The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges. Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed. A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits. The proposed algorithm results in a 12.2% reduction in energy consumption relative to standard driving practices, without a significant extension in travel time.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- speed optimization
- energy saving
- autonomous vehicles
- data-driven learning
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas