System-Level Optimization of Longitudinal Acceleration of Autonomous Vehicles in Mixed Traffic

2019 ◽  
Author(s):  
Jordan Ivanchev ◽  
David Eckhoff ◽  
Alois Knoll
Keyword(s):  
Autonomous Vehicles ◽  
System Level ◽  
Mixed Traffic ◽  
Longitudinal Acceleration

Pricing of Connected and Autonomous Vehicles in Mixed-Traffic Networks

2021 ◽  
pp. 036119812098585
Author(s):  
Paolo Delle Site
Keyword(s):  
Autonomous Vehicles ◽  
Marginal Cost ◽  
User Behavior ◽  
Social Planner ◽  
Traffic Networks ◽  
Mixed Traffic ◽  
The Third ◽  
First Case ◽  
Marginal Cost Pricing ◽  
The Social

For networks with human-driven vehicles (HDVs) only, pricing with arc-specific tolls has been proposed to achieve minimization of travel times in a decentralized way. However, the policy is hardly feasible from a technical viewpoint without connectivity. Therefore, for networks with mixed traffic of HDVs and connected and autonomous vehicles (CAVs), this paper considers pricing in a scenario where only CAVs are charged. In contrast to HDVs, CAVs can be managed as individual vehicles or as a fleet. In the latter case, CAVs can be routed to minimize the travel time of the fleet of CAVs or that of the entire fleet of HDVs and CAVs. We have a selfish user behavior in the first case, a private monopolist behavior in the second, a social planner behavior in the third. Pricing achieves in a decentralized way the social planner optimum. Tolls are not unique and can take both positive and negative values. Marginal cost pricing is one solution. The valid toll set is provided, and tolls are then computed according to two schemes: one with positive tolls only and minimum toll expenditure, and one with both tolls and subsidies and zero net expenditure. Convergent algorithms are used for the mixed-behavior equilibrium (simplicial decomposition algorithm) and toll determination (cutting plane algorithm). The computational experience with three networks: a two-arc network representative of the classic town bypass case, the Nguyen-Dupuis network, and the Anaheim network, provides useful policy insight.

Investigating the Effects of Reserved Lanes for Autonomous Vehicles on Congestion and Travel Time Reliability

2017 ◽  
Vol 2622 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Alireza Talebpour ◽  
Hani S. Mahmassani ◽  
Amr Elfar
Keyword(s):  
Travel Time ◽  
Autonomous Vehicles ◽  
Potential Benefit ◽  
Flow Dynamics ◽  
System Level ◽  
Market Penetration ◽  
Travel Time Reliability ◽  
Fundamental Diagram ◽  
Mandatory Use ◽  
Daily Travel

Autonomous vehicles are expected to influence daily travel significantly. Despite autonomous vehicles’ potential to enhance safety and to reduce congestion, energy consumption, and emissions, many studies suggest that the system-level effects will be minimal at low market penetration rates. Introducing reserved lanes for autonomous vehicles is one potential approach to address this limitation because these lanes increase autonomous vehicles’ density. However, preventing regular vehicles from using specific lanes can significantly increase congestion in other lanes. Accordingly, this study explored the potential effects of reserving one lane for autonomous vehicles on traffic flow dynamics and travel time reliability. A two-lane hypothetical segment with an on-ramp and a four-lane highway segment in Chicago, Illinois, was simulated under different market penetration rates of autonomous vehicles. Three strategies were evaluated: ( a) mandatory use of the reserved lane by autonomous vehicles, ( b) optional use of the reserved lane by autonomous vehicles, and (c) limiting autonomous vehicles to operate autonomously in the reserved lane. Policies based on combinations of these strategies were simulated. It was found that optional use of the reserved lane without any limitation on the type of operation could improve congestion and could reduce the scatter in a fundamental diagram. Throughput analysis showed the potential benefit of reserving a lane for autonomous vehicles at market penetration rates of more than 50% for the two-lane highway and 30% for the four-lane highway. Travel time reliability analysis revealed that the optional use of the reserved lane was also significantly beneficial.

scholarly journals Human-Like Lane Change Decision Model for Autonomous Vehicles that Considers the Risk Perception of Drivers in Mixed Traffic

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2259 ◽  
Author(s):  
Chang Wang ◽  
Qinyu Sun ◽  
Zhen Li ◽  
Hongjia Zhang
Keyword(s):  
Risk Perception ◽  
Autonomous Vehicles ◽  
Decision Model ◽  
Signal Detection Theory ◽  
Critical Issue ◽  
Lane Change ◽  
Mixed Traffic ◽  
Driving System ◽  
Lane Changes ◽  
The Subject

Determining an appropriate time to execute a lane change is a critical issue for the development of Autonomous Vehicles (AVs).However, few studies have considered the rear and the front vehicle-driver’s risk perception while developing a human-like lane-change decision model. This paper aims to develop a lane-change decision model for AVs and to identify a two level threshold that conforms to a driver’s perception of the ability to safely change lanes with a rear vehicle approaching fast. Based on the signal detection theory and extreme moment trials on a real highway, two thresholds of safe lane change were determined with consideration of risk perception of the rear and the subject vehicle drivers, respectively. The rear vehicle’s Minimum Safe Deceleration (MSD) during the lane change maneuver of the subject vehicle was selected as the lane change safety indicator, and was calculated using the proposed human-like lane-change decision model. The results showed that, compared with the driver in the front extreme moment trial, the driver in the rear extreme moment trial is more conservative during the lane change process. To meet the safety expectations of the subject and rear vehicle drivers, the primary and secondary safe thresholds were determined to be 0.85 m/s2 and 1.76 m/s2, respectively. The decision model can help make AVs safer and more polite during lane changes, as it not only improves acceptance of the intelligent driving system, but also further ensures the rear vehicle’s driver’s safety.

scholarly journals Future mobility as a bio-inspired collaborative system

2021 ◽  
Author(s):  
Naroa Coretti Sanchez ◽  
Juan Múgica Gonzalez ◽  
Luis Alonso Pastor ◽  
Kent Larson
Keyword(s):  
Autonomous Vehicles ◽  
Swarm Robotics ◽  
System Level ◽  
Urban Mobility ◽  
The Road ◽  
Collaborative System ◽  
Current Trends ◽  
Vehicle Platooning ◽  
On The Road ◽  
Open Question

The current trends towards vehicle-sharing, electrification, and autonomy are predicted to transform mobility. Combined appropriately, they have the potential of significantly improving urban mobility. However, what will come after most vehicles are shared, electric, and autonomous remains an open question, especially regarding the interactions between vehicles and how these interactions will impact system-level behaviour. Inspired by nature and supported by swarm robotics and vehicle platooning models, this paper proposes a future mobility in which shared, electric, and autonomous vehicles behave as a bio-inspired collaborative system. The collaboration between vehicles will lead to a system-level behaviour analogous to natural swarms. Natural swarms can divide tasks, cluster, build together, or transport cooperatively. In this future mobility, vehicles will cluster by connecting either physically or virtually, which will enable the possibility of sharing energy, data or computational power, provide services or transfer cargo, among others. Vehicles will collaborate either with vehicles that are part of the same fleet, or with any other vehicle on the road, by finding mutualistic relationships that benefit both parties. The field of swarm robotics has already translated some of the behaviours from natural swarms to artificial systems and, if we further translate these concepts into urban mobility, exciting ideas emerge. Within mobility-related research, the coordinated movement proposed in vehicle platooning models can be seen as a first step towards collaborative mobility. This paper contributes with the proposal of a framework for future mobility that integrates current research and mobility trends in a novel and unique way.

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