Connected And Automated Vehicles Technologies Research Articles

Artificial intelligence and human autonomy: the case of driving automation

AbstractThe present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated vehicles (CAVs). On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail.

Lane management for mixed traffic flow on roadways considering the car-following behaviors of human-driven vehicles to follow connected and automated vehicles

The widespread adoption of emerging connected and automated vehicles (CAVs) highlights the need for identifying the roadway capacity of mixed traffic flow with CAVs and human-driven vehicles (HDVs) for future traffic management. Previous studies focus on analyzing the impacts of CAV technologies on the mixed traffic capacity. However, research on how different car-following behaviors of HDVs to follow CAVs affect the mixed traffic capacity is still lacking. This study proposes an analytical formulation of the mixed traffic capacity based on the probability distributions of HDVs and CAVs and three types of behavior willingness (i.e., believer, neutral, and skeptic) of HDV-following-CAV in a mixed traffic environment. The sufficient conditions are derived for mixed traffic capacity to increase or decrease with the behavior willingness and CAV penetration rate. Based on that, an analytical lane management (LM) model is further proposed to determine the optimal number of CAV dedicated lanes (CDLs) and CAV non-dedicated lane (CNL) strategies that maximize the mixed traffic throughput, considering the mixed traffic demand, behavior willingness, and CAV penetration rate. The results from numerical experiments show that the proposed LM strategies can significantly improve mixed traffic throughputs by deploying optimal CDLs and CNL strategies, compared to one without the LM strategy. The mixed traffic throughputs improve and reduce as the trust willingness and skeptic willingness increase and decrease, respectively. Moreover, the increment in CAV penetration rate does not certainly correspond to the high mixed traffic throughputs. The behavior willingness and heterogeneous headways could be jointly considered to guarantee the positive influences of CAV penetration rate on the mixed traffic throughputs when implementing the LM strategy for the mixed traffic flow.

Do first responders trust connected and automated vehicles (CAVs)? A national survey

Connected and automated vehicles (CAVs) leverage emerging vehicle connectivity and automation technologies to enhance traffic safety. Extensive research has been done to examine the safety impacts of CAVs on road users (including vehicle occupants and vulnerable road users). Still, the impacts on first responders, who respond to traffic incidents and assist road users, are under-discussed. Road users show growing interest in CAVs, but it is uncertain whether first responders would feel the same way. First responders face the risk of being struck by passing vehicles when they perform their duties on road or roadside. In addition, it is unknown whether CAVs can or will do a better job than human drivers in conventional vehicles when passing an incident scene. This study conducted a national survey among first responders in the US to understand their knowledge and incident management experiences related to CAVs, as well as their attributes and concerns towards CAV technologies, including advanced driver assistance system (ADAS), connected vehicle (CV) and self-driving or autonomous vehicle (AV) technologies. Over 1000 first responders participated in the survey, and the survey had representation from all 50 states, Washington DC, and US Territories. The survey results showed that 82% of first responders have not received any CAV-related safety training, and 41% of first responders self-reported having little knowledge about CAVs. Regarding the roles of AVs in emergency responses, only a tiny portion (3%) of first responders would trust AVs more than human drivers passing an incident scene, and the majority (86%) of first responders do not think AVs will outperform human drivers. Only 1% of first responders said they trust AVs, and 44% stated that they do not trust AVs at all. A statistical model was developed to identify the correlates of first responders’ trust in AVs. Modeling results showed that education positively correlates to the likelihood of trusting AVs. This study found significant differences in perceptions towards CAVs across emergency response agencies and geographic regions. Law enforcement officers exhibit higher trust in AVs compared to firefighters; responders from DOT or public works are associated with the lowest levels of trust among all emergency response agencies. FEMA Region 3, which includes Maryland, Pennsylvania, and Virginia, shows the lowest levels of trust in AVs compared to human drivers among all regions in the country. This study provides valuable information for stakeholders to prepare responders for next-generation transportation emergency responses.

Simulation-Based Analysis of "What-If" Scenarios with Connected and Automated Vehicles Navigating Roundabouts.

Despite the potential of connected and automated vehicles (CAVs), there are still many open questions on how road capacity can be influenced and what methods can be used to assess its expected benefits in the progressive transition towards fully cooperative driving. This paper contributes to a better understanding of the benefits of CAV technologies by investigating mobility-related issues of automated vehicles operating with a cooperative adaptive cruise control system on roundabout efficiency using microscopic traffic simulation. The availability of the adjustment factors for CAVs provided by the 2022 Highway Capacity Manual allowed to adjust the entry capacity equations to reflect the presence of CAVs on roundabouts. Two mechanisms of entry maneuver based on the entry lane type were examined to compare the capacity target values with the simulated capacities. The microscopic traffic simulator Aimsun Next has been of great help in building the “what-if” traffic scenarios that we analysed to endorse hypothesis on the model parameters which affect the CAVs’ capabilities to increase roundabouts’ throughput. The results highlighted that the increasing penetration rates of CAVs have greater impacts on the operational performances of roundabouts, and provided a synthetic insight to assess the potential benefits of CAVs from an efficiency perspective.

Connected automated vehicle impacts in Southern California part-I: Travel behavior and demand analysis

Connected and automated vehicle (CAV) technologies attracted extensive attention in the past decade. As CAV brings convenience to travel, people's travel behaviors and patterns might change significantly. Existing models, however, cannot comprehensively evaluate the impacts on transportation systems. This study adopted an activity-based approach to evaluate the comprehensive CAV impacts on the transportation system in Southern California. A stated-preference survey was conducted, and captured people's behavior changes associated with CAV deployment. The model prediction demonstrated that the total trip number increased by 9%, with an 13% growth in total car-like mode travel distance. Among all trip purposes, work trips contributed to 49% of total trip number growth and 75% of the increased car-like mode travel distance. The advanced CAV technology alone wouldn’t directly benefit future transportation systems and it is still critical to have appropriate policy interventions in place.

Time-dependent on-street parking planning in a connected and automated environment

Parking remains a serious difficulty in metropolitan transport mainly because of the supply shortage in downtown areas. The parking experience will be improved with the support of connected and automated vehicle (CAV) technologies, enabled by remote parking functionality. Nevertheless, remote parking of CAVs not only results in additional surface traffic but also increases the total vehicle miles traveled (VMT). On the other hand, it is possible to manage the overall traffic by a centralized system with the assistance of CAV technology, which brings the potential for using road resources more flexibly. Inspired by the above ideas, this paper investigates a novel scheme to transfer some lanes to provide on-street parking spaces for CAVs within a necessary period to reduce “cruise-for-parking” travel; as such transfer compromises road capacity, a methodological framework is required to optimally plan the scheme for maximizing certain social goals. We formulate the on-street planning problem as a multi-period mixed-integer linear program with inter-period constraints, which determines the locations and opening durations of on-street parking lots, as well as the assignment of all parking demands across the network. Approximate approaches are adopted to solve the problem for large-scale instances. Numerical experiments are conducted on a real-world network in Wangjing District, Beijing. The results indicate that appropriate time-dependent planning of on-street parking can reduce the total VMT by 10%–27% under various demand levels.

Shared vs. dedicated lanes for automated vehicle deployment: A simulation-based assessment

• In shared lanes (SL), CAVs reduce delay and emissions by 38% and 52%, respectively. • An optimum percentage of CAV dedicated lanes (DL) is calculated for each MPR level. • SLs perform better at low traffic volumes, while DLs perform better at high volumes. • Emissions increase slightly below 40% MPR but are quickly reduced at higher MPRs. • Delay and throughput improvements occur mostly at MPRs above 30%. Connected and automated vehicles (CAV) will change the transportation world at an increasingly substantial pace. To realize the full potentials of CAV technologies, we need to investigate the transition period during which CAVs will share the roads with manually driven vehicles (MDV). This study provides a detailed assessment of the impacts of CAVs on freeways in the near future, which could serve as a basis for transportation officials and authorities to compare the different approaches for CAV deployment. The analysis was performed by modeling a hypothetical freeway segment using microsimulation. Three measures of effectiveness were used to quantify the impacts of CAVs, namely, average delay, throughput, and greenhouse gas emissions. The results reveal that at the baseline scenario of shared lanes (SL) with medium traffic volume, replacing MDVs with CAVs reduces 28.4 seconds (38%) in average delay. Additionally, throughput increases by 30%, and over 6,900 gCO 2 equivalent emissions (52%) are saved. Following the baseline analysis, an optimum percentage of dedicated lanes (DL) is derived for each market penetration rate (MPR) during the transition period. Finally, a comparison between DLs and SLs is conducted across multiple traffic volume scenarios. The findings indicate that SLs generally perform better at light traffic volumes, while DLs surpass them in congested conditions. When CAVs are deployed, both delay and throughput follow steady improvement in performance that is more evident at higher MPRs, i.e., above 30%. On the other hand, emissions experience a significant reduction in efficiency at 0 to 40% MPR but quickly make up for these losses at higher MPRs.

Ego-efficient lane changes of connected and automated vehicles with impacts on traffic flow

Connected and automated vehicles (CAVs) enabled by wireless communication and vehicle automation are believed to revolutionize the form and operation of road transport in the next decades. This paper addresses traffic flow effects of CAVs, and focuses on their lane-changing impacts on the mixed traffic flow of CAVs and human-driven vehicles (HVs). At present technical paths towards the development and deployment of CAVs are still uncertain. With CAV technologies getting matured, CAVs are supposed to provide rides of higher efficiency than HVs, beyond improved safety and comfort. In heavy traffic, this would only be achievable via agile and flexible lane changes of CAVs, because longitudinal acceleration would be unhelpful or even impossible in mixed traffic. Such lane changes are expected to be ego-efficient in that they serve solely CAVs’ interests without much considering surrounding vehicles, as long as safety constraints are not violated. As road resources are limited, the growth of the CAV population adopting such ego-efficient lane-changing strategies would probably lead to renowned “Tragedy of the Commons”. In this context, this paper considers three important prospective questions:A: How to determine an ego-efficient lane-changing strategy for CAVs?B: With increasingly more CAVs introduced each adopting the ego-efficient lane-changing strategy, what is the impact on traffic flow?C: How to determine a system-efficient lane-changing strategy for CAVs?These forward-looking issues are addressed from the perspectives of microscopic traffic simulation and reinforcement learning. Without any constraint on the lane-changing incentive, the developed lane-changing strategy was found to be beneficial for CAVs and the entire traffic flow only if the market penetration rate (MPR) of CAVs is less than 50%. With an appropriate constraint placed, however, the lane-changing strategy was found to become consistently beneficial for the entire traffic flow at any MPR. These findings suggest that CAVs may not simply be a magic cure for traffic problems that the society is currently facing, unless some upper-level coordination may be proposed for CAVs to benefit not only themselves but also the entire traffic. This is also consistent with what “Tragedy of the Commons” suggests.

Are work zones and connected automated vehicles ready for a harmonious coexistence? A scoping review and research agenda

The recent advent of connected and automated vehicles (CAVs) is expected to transform the transportation system. CAV technologies are being developed rapidly and they are foreseen to penetrate the market at a rapid pace. On the other hand, work zones (WZs) have become common areas on highway systems as a result of the increasing construction and maintenance activities. The near future will therefore bring the coexistence of CAVs and WZs which makes their interaction inevitable. WZs expose all vehicles to a sudden and complex geometric change in the roadway environment, something that may challenge many of CAV navigation capabilities. WZs however also impose a space contraction resulting in adverse traffic impacts, something that legitimately calls for benefiting from the highly efficient CAV functions. CAVs should be able to reliably traverse WZ geometry and WZs should benefit from CAV intelligent functions. This paper reviews the state-of-the-art and the key concepts, opportunities, and challenges of deploying CAV systems at WZs. The reviewed subjects include traffic performance and behaviour, technologies and infrastructure, and regulatory considerations. Eighteen CAV mobility, safety, and environmental concepts and functions were distributed over the WZ area which was subdivided into five segments: further upstream, approach area, queuing area, WZ activity, and termination area. In addition, among other topics reviewed and discussed are detection of WZ features, smart traffic control devices, various technologies at connected WZs, cross-border harmonization, liability, insurance, and privacy. The paper also provides a research agenda with a list of research needs supported by experts rating and inputs. The paper aims to provide a bird’s eye view, but with necessary details that can benefit researchers, practitioners, and transportation agencies.

Study on the Feasibility of Vehicle-to-Infrastructure Applications Supported through Advanced Wireless Communications

Connected and automated vehicle (CAV) technology has the potential to improve transportation systems. CAV technology has been shown to bring benefits in transportation mobility, safety, and the environment. The signal phase and timing (SPaT) message is a fundamental and critical CAV message, as it enables connectivity between vehicles and infrastructure. Because SPaT messages can be transmitted using different wireless communication technologies, it is necessary to study the latency and coverage of SPaT messages in such cases. This study investigates performances of SPaT messages transmitted using two popular communication technologies in CAVs: dedicated short-range communications (DSRC) and cellular 3rd generation partnership project (3GPP)/4th generation long-term evolution (4G/LTE). To provide a robust evaluation, SPaT data transmitted by DSRC and cellular networks were collected in the field at various intersections in Northern Virginia and used to compare performance in terms of latency and distance coverage. The results showed that the latency experienced by SPaT messages over cellular networks is well below the 100 ms required by most infrastructure applications, implying that cellular communications may be used for many vehicle-to-infrastructure (V2I) safety and mobility applications. The feasibility of several CAV applications was investigated based on the network performance observed in this study. Specifically, several safety, mobility, and environmental applications were tested to determine whether DSRC and cellular networks could satisfy their requirements for network performance. The study also provides conclusive remarks on whether the wireless communication technologies are capable of supporting safety, mobility, and environmental applications.

Joint Optimization of Intersection Control and Trajectory Planning Accounting for Pedestrians in a Connected and Automated Vehicle Environment

Connected and automated vehicle (CAV) technology makes it possible to track and control the movement of vehicles, thus providing enormous potential to improve intersection operations. In this paper, we study the traffic signal control problem at an isolated intersection in a CAV environment, considering mixed traffic including various types of vehicles and pedestrians. Both the vehicle delay and the pedestrian delay are incorporated into the model formulation. This introduces some additional complexity, as any benefits to pedestrians will come at the expense of higher delays for the vehicles. Thus, some valid questions we answer in this paper are as follows: Under which circumstances could we provide priority to pedestrians without over penalizing the vehicles at the intersection? How important are the connectivity and autonomy associated with CAV technology in this context? What type of signal control algorithm could be used to minimize person delay accounting for both vehicles and pedestrians? How could it be solved efficiently? To address these questions, we present a model that optimizes signal control (i.e., vehicle departure sequence), automated vehicle trajectories, and the treatment of pedestrian crossing. In each decision step, the weighted sum of the vehicle delay and the pedestrian delay (e.g., the total person delay) is minimized by the joint optimization on the basis of the predicted departure sequences of vehicles and pedestrians. Moreover, a near-optimal solution of the integrated problem is obtained with an ant colony system algorithm, which is computationally very efficient. Simulations are conducted for different demand scenarios and different CAV penetration rates. The performance of the proposed algorithm in terms of the average person delay is investigated. The simulation results show that the proposed algorithm has potential to reduce the delay compared to an actuated signal control method. Moreover, in comparison to a CAV-based signal control that does not account for the pedestrian delay, the joint optimization proposed here can achieve improvement in the low- and moderate-vehicle-demand scenarios.

Dynamic game-based approach for optimizing merging vehicle trajectories using time-expanded decision diagram

Connected and automated technologies for vehicles pave the way for major changes in traffic control methodology. A decentralized control system based on a communication network using vehicle-to-vehicle communications and each vehicle’s on-board controller will be one of the predominant approaches to vehicle trajectory optimization when the deployment of connected and automated vehicles (CAVs) has advanced, because of its scalability and fault resistance compared to the centralized system. In this study, we propose a dynamic game-based vehicle trajectory optimization approach that can be utilized in a decentralized control system based on CAV technologies for merging segments, which is one of the bottlenecks of highways. Since the decentralized system is constructed by equal individual vehicles that decide their own optimal control in conflicting situations, we introduce a game-theory-based approach for obtaining a satisfactory result for those vehicles. Further, for achieving the optimal result at the end of the target segment, we employ a dynamic game to design the whole interrelated time-series trajectories of competing pairs of vehicles. To tackle the curse of dimensionality, we use an efficient method to enumerate the sets of combinations – a zero-suppressed binary decision diagram (ZDD) – and propose an algorithm that uses this ZDD to solve the dynamic game. The performance of the proposed approach is demonstrated via numerical experiments, and is compared to a static game-based model and an individual dynamic decision model. The results prove the effectiveness of the proposed approach—it induces efficiently adjusted behaviors of competing vehicles and enables merging success, especially in a situation where the initial gaps between vehicles are not enough for immediate lane change, which is achieved by the combined effect of the game-theoretic approach and the dynamic decision approach.

Energy-aware trajectory optimization of CAV platoons through a signalized intersection

Traffic signals, while serving an important function to coordinate vehicle movements through intersections, also cause frequent stops and delays, particularly when they are not properly timed. Such stops and delays contribute to significant amount of fuel consumption and greenhouse gas emissions. The recent development of connected and automated vehicle (CAV) technology provides new opportunities to enable better control of vehicles and intersections, that in turn reduces fuel consumption and emissions. In this paper, we propose a trajectory optimization method, PTO-GFC, to reduce the total fuel consumption of a CAV platoon through a signalized intersection. In this method, we first apply platoon-trajectory-optimization (PTO) to obtain the optimal trajectories of the platoon vehicles. In PTO, all CAVs in one platoon are considered as a whole, that is, all other CAVs follow the trajectory of the leading one with a time delay and minimum safety gap, which is enabled by vehicle to vehicle communication. Then, we apply gap-feedback-control (GFC) to control the vehicles with different speeds and headways merging into the optimal trajectories. We compare the PTO-GFC method with the other two methods, in which the leading vehicle adopts the optimal trajectory (LTO) or drive with maximum speed (AT), respectively, and the other vehicles follow the leading vehicle with a simplified Gipps’ car-following model. Furthermore, we extend the controls into multiple platoons by considering the interactions between the two platoons. The numerical results demonstrate that PTO-GFC has better performance than LTO and AT, particularly when CAVs have enough space and time to smooth their trajectories. The reduction of travel time and fuel consumption shows the great potential of CAV technology in reducing congestion and negative environmental impact of automobile transportation.

Characterisation of the impacts of autonomous driving on highway capacity in a mixed traffic environment: an agent‐based approach

The current planning and operational analysis tools utilised by researchers and practitioners for transportation facilities evaluation purposes are limited in their relevance to evaluate the future impact of connected and automated vehicles (CAVs). Since CAV technologies are becoming more prevalent on the market, the adaptation of the conventional methodologies to estimate highway performance is becoming essential to accommodate these technologies. This study tries to fill up this gap in the literature by formulating capacity analysis model estimate the potential impacts of CAVs on the capacity of highways at different market penetration (MP) levels of CAVs. The developed analytical model would present itself as a guideline to transportation agencies while developing CAV-based transportation infrastructure. Later, this study proposes an agent-based modelling and simulation framework to address the heterogeneity of drivers behaviour and the potential impact of CAVs on highway traffic performance under different MP levels. Results showed that deploying CAVs in the traffic stream has the potential to double the capacity of the highway corridor at high MP levels. The results show CAV operation and implementation readiness for both public agencies as well as state Departments of Transportation or private industries in the future.

Influence of red-light violation warning systems on driver behavior – a driving simulator study

Objective: With the advent of connected and automated vehicle (CAV) technology, there is an increasing need to evaluate driver behavior with the advent of connected and automated vehicle (CAV) technology. This study introduced a red-light violation warning (RLVW) system using CAV technology in a driving simulator environment, to evaluate driver braking behavior when the light changes suddenly from green to yellow.Methods: Researchers recruited a total of 93 participants from diverse socio-economic backgrounds for this study and created a virtual network of downtown Baltimore. An eye tracking device was used to observe distractions and head movements. A Lognormal accelerated failure time (AFT) distribution model was used for this analysis, to calculate speed reduction times from the moment the traffic light changes from green to yellow, to the point where a minimum speed was reached.Results: It was observed that speed reduction times were significantly higher in the presence of a RLVW system, requiring a longer period of time to come to a complete stop at the red light. Inferences can be drawn from the jerk analysis that, the RLVW system results in a highly unsafe jerk at the onset of the warning. Without the RLVW system though, a highly uncomfortable positive jerk occurs closer to the signal, which is due to sudden acceleration, as the participants possibly slowed down a lot initially. Gaze analysis showed that the system was able to attract the attention of the drivers, as the majority of the drivers noticed the displayed warning.Conclusions: The findings suggest that the presence of an RLVW system sends a clear message to the driver about the change in traffic light and gives the driver ample time to adapt their initial approach speed to stop at the signal, avoiding potential intersection crashes.

Leveraging existing high-occupancy vehicle lanes for mixed-autonomy traffic management with emerging connected automated vehicle applications

Transportation agencies have started including phased deployment of connected and automated vehicle (CAV) technologies in their transportation plans and programs. While theoretical analyses have indicated significant benefits of CAVs for improving system performance, deploying these technologies at existing or adapted highway facilities concerns more than technological issues. This study introduces the concepts and operational strategies of using managed lanes, High-Occupancy Vehicle (HOV) lanes in particular, for mixed-autonomy traffic management. The study enhances existing and develops new algorithms for three selected freeway CAV applications as a CAV technology integration, including speed harmonization, cooperative adaptive cruise control (CACC), and cooperative merge. Instead of evaluation on a synthetic segment, this study reports a large-scale simulation-based real-world case study to investigate CAV early deployment opportunities on Interstate 66 outside the Beltway. The simulation results show that, for all scenarios, individual and bundled CAV applications can significantly improve traffic performance in terms of delay and throughput. CACC platooning is the most effective individual strategy to improve traffic performance. The bundled CAV applications can benefit the system, even with low CAV market penetration, and fully handle more than 130 percent of the existing demand with high CAV market penetration rates. Additionally, left-side dedicated ramps and shared managed lane operational strategies are also beneficial, even during early deployment stages.

A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles

Connected and automated vehicles (CAVs) have the potential to address a number of safety, mobility, and sustainability issues of our current transportation systems. Cooperative longitudinal motion control is one of the key CAV technologies that allows vehicles to be driven in a cooperative manner to achieve system-wide benefits. In this paper, we provide a literature survey on the progress accomplished by researchers worldwide regarding cooperative longitudinal motion control systems of multiple CAVs. Specifically, the architecture of various cooperative CAV systems is reviewed to answer how cooperative longitudinal motion control can work with the help of multiple system modules. Next, different operational concepts of cooperative longitudinal motion control applications are reviewed to answer where they can be implemented in today's transportation systems . Different cooperative longitudinal motion control methodologies and their major characteristics are then described to answer what the critical design issues are . This paper concludes by describing an overall landscape of cooperative longitudinal motion control of CAVs, as well as pointing out opportunities and challenges in the future research and experimental implementations.

Hardware-in-the-Loop Testing of Connected and Automated Vehicle Applications: A Use Case for Queue-Aware Signalized Intersection Approach and Departure

Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptions limit the validity of evaluation results. One alternative approach is to use emerging hardware-in-the-loop (HIL) testing methods. HIL test environments enable physical test vehicles to interact with virtual vehicles from traffic simulation models, providing an evaluation environment that can replicate deployment conditions at early stages of CAV technology implementation without incurring excessive costs related to large field tests. In this study, a HIL testing system for vehicle-to-infrastructure (V2I) CAV applications is developed. The involved software and hardware includes a physical CAV controlled in real time, a traffic signal controller, communication devices, and a traffic simulator (VISSIM). Such HIL systems increase validity by considering the physical vehicle’s trajectories—which are constrained by real-world factors such as GPS accuracy, communication delay, and vehicle dynamics—in a simulated traffic environment. The developed HIL system is applied to test a representative early deployment CAV application: queue-aware signalized intersection approach and departure (Q-SIAD). The Q-SIAD algorithm generates recommended speed profiles based on the vehicle’s status, signal phase and timing (SPaT), downstream queue length, and system constraints and parameters (e.g., maximum acceleration and deceleration). The algorithm also considers the status of other vehicles in designing the speed profiles. The experiment successfully demonstrated this functionality with one test CAV driving through one intersection controlled by a fixed-timing traffic signal under various simulated traffic conditions.

Connected Vehicles and Powertrain Optimization

This article discusses the basic concept of connected and automated vehicles (CAV) technology. The common methods to improve fuel economy are also introduced. The effects of connectivity on vehicle/powertrain control and optimization are also elaborated. The Michigan Tech NEXTCAR project is also presented to provide a more detailed view of predictive vehicle/powertrain control enabled by CAV technologies. The U.S. Department of transportation (DOT) and other federal/state funding agencies have supported research and pilot deployment efforts to develop crosscutting CV technologies and evaluate the effectiveness of CV technologies in real-world transportation systems. The concurrent development of connected and automated vehicle technologies is anticipated to provide synergistic benefits to improve traffic safety, mobility, and energy efficiency. It is observed that increased CAV technologies are being deployed in real-world transportation systems.