[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Kavas-Torris et al., 2023 - Google Patents

The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks

Kavas-Torris et al., 2023

View PDF
Document ID
4065078489381695376
Author
Kavas-Torris O
Cantas M
Cime K
Aksun-Guvenc B
Guvenc L
Publication year
Publication venue
arXiv preprint arXiv:2306.01177

External Links

Snippet

Microscopic traffic simulators that simulate realistic traffic flow are crucial in studying, understanding and evaluating the fuel usage and mobility effects of having a higher number of autonomous vehicles (AVs) in traffic under realistic mixed traffic conditions including both …
Continue reading at arxiv.org (PDF) (other versions)

Similar Documents

Publication Publication Date Title
Xie et al. Distributed motion planning for safe autonomous vehicle overtaking via artificial potential field
Guo et al. Hybrid deep reinforcement learning based eco-driving for low-level connected and automated vehicles along signalized corridors
Kavas-Torris et al. The Effects of Varying Penetration Rates of L4-L5 Autonomous Vehicles on Fuel Efficiency and Mobility of Traffic Networks
Bai et al. Hybrid reinforcement learning-based eco-driving strategy for connected and automated vehicles at signalized intersections
Zhang et al. A game theoretic model predictive controller with aggressiveness estimation for mandatory lane change
Wang et al. Developing a platoon-wide eco-cooperative adaptive cruise control (CACC) system
Hu et al. CACC simulation platform designed for urban scenes
Kavas-Torris et al. Fuel economy benefit analysis of pass-at-green (PaG) V2I application on urban routes with STOP signs
Tamilarasan et al. Impact of different desired velocity profiles and controller gains on convoy driveability of cooperative adaptive cruise control operated platoons
Gupta et al. Eco-driving of connected and autonomous vehicles with sequence-to-sequence prediction of target vehicle velocity
Gelbal et al. Cooperative collision avoidance in a connected vehicle environment
Prakash et al. Use of the hypothetical lead (hl) vehicle trace: A new method for evaluating fuel consumption in automated driving
Cantas et al. Cooperative adaptive cruise control design and implementation
Cantas et al. Customized co-simulation environment for autonomous driving algorithm development and evaluation
Wang et al. Agent-based modeling and simulation of connected and automated vehicles using game engine: A cooperative on-ramp merging study
Deshpande et al. In-vehicle test results for advanced propulsion and vehicle system controls using connected and automated vehicle information
Turri Look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning
Ferrarotti et al. Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation
Koch et al. Adaptive Traffic Light Control With Deep Reinforcement Learning: An Evaluation of Traffic Flow and Energy Consumption
Jang et al. Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test
Kavas-Torris et al. A Comprehensive Eco-Driving Strategy for Connected and Autonomous Vehicles (CAVs) with Microscopic Traffic Simulation Testing Evaluation
Li et al. Decision making for autonomous vehicles
Wei et al. A learning-based autonomous driver: emulate human driver's intelligence in low-speed car following
Pariota et al. Motivating the need for an integrated software architecture for connected and automated vehicles technologies development and testing
Gelbal et al. Virtual and Real Data Populated Intersection Visualization and Testing Tool for V2X Application Development