Fully autonomous vehicles (AV) would potentially be one of the most disruptive technologies of our time. The extent of the prospective benefits of AVs is strongly linked to how widely they will be ...accepted and adopted. Monitoring and tracking of individuals' reactions and intentions to use AVs are critical. The current study aims to explore and classify individual predictors (i.e., influential factors or determinants) of public acceptance of, and intention to use AVs, by conducting a systematic literature review and developing a conceptual framework to map out the individual influential factors that shape public attitudes towards AVs, which influence user acceptance and adoption preferences. This framework contains the key factors identified in the systematic review-i.e., demographic, psychological, and mobility behavior characteristics. The findings of the review disclose that public perceptions and adoption intentions vary significantly among different socio-demographic cohorts. Commuters value different aspects concerning AVs, which shape their intentions on acceptance and adoption. Thus, direct experience with AVs along with education and communication would be helpful to change people's attitudes towards AVs in a positive way. The study informs urban and transport policymakers, managers, and planners, and helps in planning for a healthy AV adoption process with minimal societal disruption.
Shared autonomous (fully-automated) vehicles (SAVs) represent an emerging transportation mode for driverless and on-demand transport. Early actors include Google and Europe’s CityMobil2, who seek ...pilot deployments in low-speed settings. This work investigates SAVs’ potential for U.S. urban areas via multiple applications across the Austin, Texas, network. This work describes advances to existing agent- and network-based SAV simulations by enabling dynamic ride-sharing (DRS, which pools multiple travelers with similar origins, destinations and departure times in the same vehicle), optimizing fleet sizing, and anticipating profitability for operators in settings with no speed limitations on the vehicles and at adoption levels below 10 % of all personal trip-making in the region. Results suggest that DRS reduces average service times (wait times plus in-vehicle travel times) and travel costs for SAV users, even after accounting for extra passenger pick-ups, drop-offs and non-direct routings. While the base-case scenario (serving 56,324 person-trips per day, on average) suggest that a fleet of SAVs allowing for DRS may result in vehicle-miles traveled (VMT) that exceed person-trip miles demanded (due to anticipatory relocations of empty vehicles, between trip calls), it is possible to reduce overall VMT as trip-making intensity (SAV membership) rises and/or DRS users become more flexible in their trip timing and routing. Indeed, DRS appears critical to avoiding new congestion problems, since VMT may increase by over 8 % without any ride-sharing. Finally, these simulation results suggest that a private fleet operator paying $70,000 per new SAV could earn a 19 % annual (long-term) return on investment while offering SAV services at $1.00 per mile for a non-shared trip (which is less than a third of Austin’s average taxi cab fare).
•We analyze Austinites’ opinions on connected and autonomous vehicles (CAVs).•We estimate their willingness to pay (WTP) for CAVs and related technologies.•Their average WTP for Level 3 automation is ...$3300, versus $7253 for Level 4.•Higher-income, tech-savvy males in denser settings are more interested in CAVs.•41% willing to use shared AVs at least once a week at $1/mile, vs. 15% at $2/mile.
Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever-evolving transportation system. Anticipating public acceptance and adoption of these technologies is important. A recent internet-based survey polled 347 Austinites to understand their opinions on smart-car technologies and strategies. Results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7253) appears to be much higher than that for adding partial (Level 3) automation ($3300) to their current vehicles.
Ordered probit and other model specifications estimate the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding various automation technologies and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, who live in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.
•AV/toll lanes are proposed as a promising alternative to dedicated AV lanes.•Joint use of dedicated AV lanes and AV/toll lanes can better improve the system efficiency.•A multi-class user ...equilibrium model is proposed to describe the flow distribution.•Non-uniqueness of O-D equilibrium travel cost and system performance is identified.•A robust optimization model is developed for the strategic planning of AV and AV/toll lanes.
Employing vehicle communication and automated control technologies, autonomous vehicles (AVs) can safely drive closer together than human-driven vehicles (HVs), thereby potentially improving traffic efficiency. Separation between AV and HV traffic through the deployment of dedicated AV lanes is foreseen as an effective method of amplifying the benefits of AVs and promoting their adoption. However, it is important to consider mixed AV and HV traffic in a transportation network. On the one hand, it may be impractical to deploy dedicated AV lanes throughout the network, while on the other hand, dedicated AV lanes may even reduce the total traffic efficiency of a road segment when the AV flow rate is low. In this study, we considered a new form of managed lanes for AVs, designated as autonomous vehicle/toll (AVT) lanes, which grant free access to AVs while allowing HVs to access the lanes by paying a toll. We investigated the optimal deployment of dedicated AV lanes and AVT lanes in transportation networks with mixed AV and HV flows. The user equilibrium (UE) problem in a transportation network with mixed flows of AVs and HVs is first explored. We formulated the UE problem as a link-based variational inequality (VI) and identified that, with different impacts of AVs on road capacity, the UE problem can have unique or non-unique flow patterns. Considering that the UE problem may have non-unique flow distributions, we proposed a robust optimal deployment model, which is a generalized semi-infinite min-max program, to deploy the dedicated AV lanes and AVT lanes so that the system performance under the worst-case flow distributions is optimized. We proposed effective solution algorithms to solve these models and presented numerical studies to demonstrate the models and the solution algorithms. The results show that the system performance can be significantly improved through the deployment of dedicated AV lanes and AVT lanes.
Safety is the most important requirement for autonomous vehicles; hence, the ultimate challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver enough computing power, ...redundancy, and security so as to guarantee the safety of autonomous vehicles. Specifically, autonomous driving systems are extremely complex; they tightly integrate many technologies, including sensing, localization, perception, decision making, as well as the smooth interactions with cloud platforms for high-definition (HD) map generation and data storage. These complexities impose numerous challenges for the design of autonomous driving edge computing systems. First, edge computing systems for autonomous driving need to process an enormous amount of data in real time, and often the incoming data from different sensors are highly heterogeneous. Since autonomous driving edge computing systems are mobile, they often have very strict energy consumption restrictions. Thus, it is imperative to deliver sufficient computing power with reasonable energy consumption, to guarantee the safety of autonomous vehicles, even at high speed. Second, in addition to the edge system design, vehicle-to-everything (V2X) provides redundancy for autonomous driving workloads and alleviates stringent performance and energy constraints on the edge side. With V2X, more research is required to define how vehicles cooperate with each other and the infrastructure. Last, safety cannot be guaranteed when security is compromised. Thus, protecting autonomous driving edge computing systems against attacks at different layers of the sensing and computing stack is of paramount concern. In this paper, we review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges.
•Develop a cooperative platoon control for a mixed flow platoon including CAVs and HDVs.•Integrate a curving matching algorithm for detecting HDV behavior online.•Develop MPCs involving system ...optimizers to ensure the optimal performance of the entire platoon.•Develop distribution algorithms to solve optimizers with multiple variables and coupled constraints.
This study seeks to develop a cooperative platoon control for a platoon mixed with connected and autonomous vehicles (CAVs) and human-drive vehicles (HDVs), aiming to ensure system level traffic flow smoothness and stability as well as individual vehicles’ mobility and safety. Specifically, our study integrated/contributed the following technical approaches. First, the car-following behavior of human-drive vehicles is modeled by well-accepted Newell car-following models. Accordingly, an online curve matching algorithm is integrated to anticipate the aggregated response delay of the human-drive vehicles using real-time trajectory data. Built upon that, constrained One- or P-step MPC models are developed to control the movement of the CAV platoon upstream or downstream of the HDV platoon so that we can ensure both transient traffic smoothness and asymptotic stability of this sample mixed flow platoon, leveraging the communication and computation technologies equipped on CAVs. Considering the lack of the centralized computation facilities and severe changes of the platoon topology, this study develops a distributed algorithm to solve the MPCs according to the properties of the optimizers, such as solution uniqueness, sequentially feasibility, and nonempty interior point of the solution space. The convergence of the distributed algorithm as well as the stability of the MPC control is proved by both the theoretical analysis and the experimental study. Extensive numerical experiments based on the field data indicate that the distributed algorithm can solve the One-step and P-step MPCs efficiently. The cooperative MPC can dampen traffic oscillation propagation and stabilize the traffic flow more efficiently for the entire mixed flow platoon. Moreover, the cooperative platoon control scheme outperforms the other three control strategies, including the non-cooperative control strategy and a latest CACC strategy in literature.
•Comprehensively reviewed major methods for analyzing local stability of CF models.•Comprehensively reviewed major methods for analyzing string stability of CF models.•Considered basic, time-delayed, ...and multi-anticipative/cooperative CF models.•Assessed consistency and applicability of several stability criteria.•Discussed issues, challenges, and research needs.
The paper comprehensively reviews major methods for analysing local and string stability of car-following (CF) models. Specifically, three types of CF models are considered: basic, time-delayed, and multi-anticipative/cooperative CF models. For each type, notable methods in the literature for analysing its local stability and string stability have been reviewed in detail, including the characteristic equation based method (e.g., root extracting, the root locus method, the Routh–Hurwitz criterion, the Nyquist criterion and the Hopf bifurcation method), Lyapunov criterion, the direct transfer function based method, and the Laplace transform based method. In addition, consistency and applicability of stability criteria obtained using some of these methods are objectively compared with the simulation result from a series of numerical experiments. Finally, issues, challenges, and research needs of CF models’ stability analysis in the era of connected and autonomous vehicles are discussed.
Compared to existing human-driven vehicles (HDVs), connected and autonomous vehicles (CAVs) offer users the potential for reduced value of time, enhanced quality of travel experience, and seamless ...situational awareness and connectivity. Hence, CAV users can differ in their route choice behavior compared to HDV users, leading to mixed traffic flows that can significantly deviate from the single-class HDV traffic pattern. However, due to the lack of quantitative models, there is limited knowledge on the evolution of mixed traffic flows in a traffic network. To partly bridge this gap, this study proposes a multiclass traffic assignment model, where HDV users and CAV users follow different route choice principles, characterized by the cross-nested logit (CNL) model and user equilibrium (UE) model, respectively. The CNL model captures HDV users' uncertainty associated with limited knowledge of traffic conditions while overcoming the route overlap issue of logit-based stochastic user equilibrium. The UE model characterizes the CAV's capability for acquiring accurate information on traffic conditions. In addition, the multiclass model can capture the characteristics of mixed traffic flow such as the difference in value of time between HDVs and CAVs and the asymmetry in their driving interactions, thereby enhancing behavioral realism in the modeling. The study develops a new solution algorithm labeled RSRS-MSRA, in which a route-swapping based strategy is embedded with a self-regulated step size choice technique, to solve the proposed model efficiently. Sensitivity analysis of the proposed model is performed to gain insights into the effects of perturbations on the mixed traffic equilibrium, which facilitates the estimation of equilibrium traffic flow and identification of critical elements under expected or unexpected events. The study results can assist transportation decision-makers to design effective planning and operational strategies to leverage the advantages of CAVs and manage traffic congestion under mixed traffic flows.
•Systematic car-following control algorithm for a connected and autonomous vehicle platoon.•Distributed algorithm solving a convex optimization with coupled constraints.•Stability analysis to ...demonstrate the desired transient and asymptotic dynamics.
Motivated by the advancement in connected and autonomous vehicle technologies, this paper develops a novel car-following control scheme for a platoon of connected and autonomous vehicles on a straight highway. The platoon is modeled as an interconnected multi-agent dynamical system subject to physical and safety constraints, and it uses the global information structure such that each vehicle shares information with all the other vehicles. A constrained optimization based control scheme is proposed to ensure an entire platoon’s transient traffic smoothness and asymptotic dynamic performance. By exploiting the solution properties of the underlying optimization problem and using primal-dual formulation, this paper develops dual based distributed algorithms to compute optimal solutions with proven convergence. Furthermore, the asymptotic stability of the unconstrained linear closed-loop system is established. These stability analysis results provide a principle to select penalty weights in the underlying optimization problem to achieve the desired closed-loop performance for both the transient and the asymptotic dynamics. Extensive numerical simulations are conducted to validate the efficiency of the proposed algorithms.
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