Speed Trajectory Generation for Energy-Efficient Connected and Automated Vehicles

2020 ◽  
Author(s):  
Lung En Jan ◽  
Junfeng Zhao ◽  
Shunsuke Aoki ◽  
Anand Bhat ◽  
Chen-Fang Chang ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Network Models ◽  
Autonomous Driving ◽  
Urban Traffic ◽  
Sensor Data ◽  
Driver Model ◽  
Automated Vehicles ◽  
Vehicle Trajectories ◽  
Planning Framework ◽  
Vehicle To Infrastructure

Abstract Connected and automated vehicles (CAVs) have real-time knowledge of the immediate driving environment, actions to be taken in the near future and information from the cloud. This knowledge, referred to as preview information, enables CAVs to drive safely, but can also be used to minimize fuel consumption. Such fuel-efficient transportation has the potential to reduce aggregate fuel consumption by billions of gallons of gas every year in the U.S. alone. In this paper, we propose a planning framework for use in CAVs with the goal of generating fuel-efficient vehicle trajectories. By utilizing on-board sensor data and vehicle-to-infrastructure (V2I) communications, we leverage the computational power of CAVs to generate eco-friendly vehicle trajectories. The planner uses an eco-driver model and a predictive cost-based search to determine the optimal speed profile for use by a CAV. To evaluate the performance of the planner, we introduce a co-simulation environment consisting of a CAV simulator, Matlab/Simulink and a CAV software platform called the InfoRich Eco-Autonomous Driving (iREAD) system. The planner is evaluated in various urban traffic scenarios based on real-world road network models provided by the National Renewable Energy Laboratory (NREL). Simulations show an average savings of 14.5% in fuel consumption with a corresponding increase of 2% in travel time using our method.

scholarly journals Relationship between heavy vehicle speed limit and fleet fuel consumption on minor roads

2015 ◽  
Vol 230 (9) ◽  
pp. 1461-1478
Author(s):  
G Wilson ◽  
G Morrison ◽  
W Midgley ◽  
D Cebon
Keyword(s):  
Fuel Consumption ◽  
Traffic Safety ◽  
Traffic Congestion ◽  
Driver Model ◽  
Speed Limit ◽  
Vehicle Speed ◽  
Selection Scheme ◽  
Vehicle Trajectories ◽  
Model Fuel ◽  
Heavy Goods Vehicles

This paper investigates the traffic-related effects of a proposal to increase the speed limit from 40 mile/h to 50 mile/h, for heavy goods vehicles greater than 7.5 tonnes, on single carriageway roads. A ‘microscopic’ single carriageway traffic simulation is developed by combining the ‘enhanced intelligent driver model’ with a single carriageway gap-acceptance passing model. Fuel consumption estimates are made using engine characteristic maps and a ‘fuel optimal’ gear selection scheme, where vehicle trajectories from the traffic simulations are taken as input drive-cycles. Traffic congestion and fleet fuel consumption are specifically addressed, though implications regarding passing behaviour and traffic safety are also noted. Results indicate that the proposed 50 mile/h heavy goods vehicles speed limit would reduce traffic congestion by over 37% and increase fleet fuel consumption by approximately 0.5 L/100 km.

scholarly journals Design and Numerical Implementation of V2X Control Architecture for Autonomous Driving Vehicles

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1696
Author(s):  
Piyush Dhawankar ◽  
Prashant Agrawal ◽  
Bilal Abderezzak ◽  
Omprakash Kaiwartya ◽  
Krishna Busawon ◽  
...  
Keyword(s):  
Control System ◽  
Autonomous Driving ◽  
Driver Model ◽  
Ieee 802.11P ◽  
Packet Error Rate ◽  
Control Architecture ◽  
Practical Implementation ◽  
Infinite Length ◽  
Estimation Model ◽  
Vehicle To Infrastructure

This paper is concerned with designing and numerically implementing a V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) control system architecture for a platoon of autonomous vehicles. The V2X control architecture integrates the well-known Intelligent Driver Model (IDM) for a platoon of Autonomous Driving Vehicles (ADVs) with Vehicle-to-Infrastructure (V2I) Communication. The main aim is to address practical implementation issues of such a system as well as the safety and security concerns for traffic environments. To this end, we first investigated a channel estimation model for V2I communication. We employed the IEEE 802.11p vehicular standard and calculated path loss, Packet Error Rate (PER), Signal-to-Noise Ratio (SNR), and throughput between transmitter and receiver end. Next, we carried out several case studies to evaluate the performance of the proposed control system with respect to its response to: (i) the communication infrastructure; (ii) its sensitivity to an emergency, inter-vehicular gap, and significant perturbation; and (iii) its performance under the loss of communication and changing driving environment. Simulation results show the effectiveness of the proposed control model. The model is collision-free for an infinite length of platoon string on a single lane road-driving environment. It also shows that it can work during a lack of communication, where the platoon vehicles can make their decision with the help of their own sensors. V2X Enabled Intelligent Driver Model (VX-IDM) performance is assessed and compared with the state-of-the-art models considering standard parameter settings and metrics.

scholarly journals Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3783
Author(s):  
Sumbal Malik ◽  
Manzoor Ahmed Khan ◽  
Hesham El-Sayed
Keyword(s):  
Autonomous Vehicles ◽  
Communication Technologies ◽  
Autonomous Driving ◽  
Use Cases ◽  
Cooperative Driving ◽  
Vehicle To Vehicle ◽  
Future Challenges ◽  
Vehicle To Infrastructure ◽  
High Level ◽  
Intersection Management

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

scholarly journals Impact of External Human–Machine Interface Communication Strategies of Automated Vehicles on Pedestrians’ Crossing Decisions and Behaviors in an Urban Environment

2021 ◽  
Vol 13 (15) ◽  
pp. 8396
Author(s):  
Marc Wilbrink ◽  
Merle Lau ◽  
Johannes Illgner ◽  
Anna Schieben ◽  
Michael Oehl
Keyword(s):  
Urban Traffic ◽  
Automated Vehicles ◽  
Negative Effects ◽  
Communication Tools ◽  
Automated Vehicle ◽  
Additional Information ◽  
Road Users ◽  
Vulnerable Road Users ◽  
And Behaviors ◽  
Vehicle Automation

The development of automated vehicles (AVs) and their integration into traffic are seen by many vehicle manufacturers and stakeholders such as cities or transportation companies as a revolution in mobility. In future urban traffic, it is more likely that AVs will operate not in separated traffic spaces but in so-called mixed traffic environments where different types of traffic participants interact. Therefore, AVs must be able to communicate with other traffic participants, e.g., pedestrians as vulnerable road users (VRUs), to solve ambiguous traffic situations. To achieve well-working communication and thereby safe interaction between AVs and other traffic participants, the latest research discusses external human–machine interfaces (eHMIs) as promising communication tools. Therefore, this study examines the potential positive and negative effects of AVs equipped with static (only displaying the current vehicle automation status (VAS)) and dynamic (communicating an AV’s perception and intention) eHMIs on the interaction with pedestrians by taking subjective and objective measurements into account. In a Virtual Reality (VR) simulator study, 62 participants were instructed to cross a street while interacting with non-automated (without eHMI) and automated vehicles (equipped with static eHMI or dynamic eHMI). The results reveal that a static eHMI had no effect on pedestrians’ crossing decisions and behaviors compared to a non-automated vehicle without any eHMI. However, participants benefit from the additional information of a dynamic eHMI by making earlier decisions to cross the street and higher certainties regarding their decisions when interacting with an AV with a dynamic eHMI compared to an AV with a static eHMI or a non-automated vehicle. Implications for a holistic evaluation of eHMIs as AV communication tools and their safe introduction into traffic are discussed based on the results.

scholarly journals Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shan Fang ◽  
Lan Yang ◽  
Tianqi Wang ◽  
Shoucai Jing
Keyword(s):  
Fuel Consumption ◽  
Trajectory Planning ◽  
Signalized Intersections ◽  
Planning Method ◽  
Signalized Intersection ◽  
Driver Model ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Traffic Lights ◽  
Trigonometric Model

Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.

scholarly journals Automated futures and the mobile present: In-car video ethnographies

Ethnography ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 88-107 ◽  
Author(s):  
Sarah Pink ◽  
Vaike Fors ◽  
Mareike Glöss
Keyword(s):  
Research Agenda ◽  
Human Mobility ◽  
Methodological Approach ◽  
Autonomous Driving ◽  
Automated Vehicles ◽  
Video Ethnography ◽  
Anthropological Theory ◽  
The Future

New technological possibilities associated with autonomous driving (AD) cars are generating new questions and imaginaries about automated futures. In this article we advance a theoretical-methodological approach towards researching this context based in design anthropological theory and sensory ethnographic practice. In doing so we explain and discuss the findings of an in-car video ethnography study designed to investigate the usually unspoken and not necessarily visible elements of car-based mobility. Such an approach is needed, we argue, both in order to inform a research agenda that is capable of addressing the emergence of automated vehicles specifically, as well as in preparation for understanding the implications of automation more generally as human mobility is increasingly entangled with automated technologies and the future imaginaries associated with them.

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