Policies and Design Guidelines to Plan for Connected and Autonomous Vehicles

Evaluating the safety impact of connected and autonomous vehicles on motorways

Optimal deployment of an equitable CAV platoonable corridor on road networks with mixed traffic flow

With the accelerated integration of connected and autonomous vehicles (CAVs) into transportation systems, such vehicles have evolved rapidly. Emerging studies have focused on the considerations influencing travelers’ acceptance of CAVs and the factors contributing to travel choice intention. However, the different reasons for the diffusion of CAVs and the associated trends in relation to this are key questions that still require investigation. Therefore, this study establishes a structural equation modeling (SEM) framework to explore the impact of both external factors and internal subjective factors on travel choice. Based on the influencing factors identified by SEM, this study develops an agent-based model simulation approach to examine the diffusion trends in relation to CAV travelers from an individual perspective. To calibrate the model, a questionnaire survey is designed to obtain data from travelers in Shenzhen, Guangdong Province, China. The survey results show that CAV choice intention is influenced by individual (including innovativeness), travel-related, and social influence factors. The simulation experiments reveal that the diffusion of CAV travelers is a complex process. The lower cost of CAV travel has a positive impact initially and midway through the process of diffusion in relation to CAV users. For example, in situations in which CAVs are more cost-effective, during the simulation time (years) the number of CAV users fluctuates significantly between the 7th and 30th years. There is a notable increase of 10% in CAV users in the 15th year and, eventually, the total number of CAV users stabilizes at 71.8% of all travelers. The findings will assist agencies and CAV operators to implement effective promotion strategies.

The lack of real-world data on Connected and Autonomous Vehicles (CAVs) has prompted researchers to rely on simulations to assess their societal impacts. However, few studies address the operational and technological challenges of integrating CAVs into existing transport systems. This paper introduces a new CAV driving model featuring a constant time gap longitudinal control algorithm that accounts for sensor errors and platoon formations of varying sizes. Additionally, it develops a high-level route-based decision-making algorithm for CAV path choice. These algorithms were tested in a calibrated motorway corridor simulation, examining different market penetration rates, platoon sizes, and sensor error scenarios. Traffic conflicts were used as a primary safety performance indicator. The findings indicate that CAV sensors are generally adequate, but optimal platoon sizes vary with market penetration rates. To further explore factors influencing traffic conflicts, a hierarchical Bayesian negative binomial regression model was used. This model revealed that in addition to unobserved heterogeneity and spatial autocorrelation, the standard deviation of speeds between lanes and the CAV market penetration rate significantly affect conflict occurrences. These results corroborate the simulation outcomes, enhancing our understanding of CAV deployment impacts on traffic safety.

One goal for large-scale deployment of connected and autonomous vehicles is to achieve the traffic safety benefit since connected and autonomous vehicles (CAVs) could reduce the collision risk by enhancing the driver’s situation perception ability. Previous studies have analyzed the safety impact of CAVs involved in traffic, but only few studies examined the safety benefits brought by CAVs when approaching high-collision-risk road segments such as the freeway crash hotspots. This study chooses one freeway crash hotspot in Wuhan, China, as an instance and attempts to estimate the safety benefits for differential penetration rates (PRs) of CAVs using the surrogate safety assessment model (SSAM). First, the freeway crash hotspot is identified with kernel density estimation and simulated by VISSIM. Then, the intelligent driver model (IDM) and Wiedemann 99 (a car-following model) are adopted and calibrated to control the driving behaviors of CAVs and human-driven vehicles (HVs) in this study, respectively. The impact that rather CAVs are constrained with or without managed lanes on traffic safety is also discussed, and the PR of CAVs is set from 10% to 90%. The results of this study show that when the PR of CAVs is lower than 50%, there is no significant improvement on the safety measures such as conflicts, acceleration, and velocity difference, which are extracted from the vehicle trajectory data using SSAM. When the penetration rate is over 70%, the experiment results demonstrate that the traffic flow passing the freeway hotspot is with fewer conflicts, smaller acceleration, and smaller velocity difference in the scenario where CAVs are constrained with managed lane compared with the scenario without managed lane control. The safety benefit that CAVs bring needs to be discussed. The lane management of CAVs will also lead to distinct safety impact.

Vehicle-to-infrastructure (V2I) communication is becoming indispensable for successful roll-out of connected and autonomous vehicles (CAVs). While increasing the CAVs’ speed improves the average CAV traffic flow, it increases communication handoffs (HOs) thus reducing wireless data rates. Furthermore, unplanned density of active base-stations (BSs) may result in severe interference which negatively impacts CAV data rate. In this letter, we first characterize macroscopic traffic flow by considering log-normal distribution of the spacing between CAVs. We then derive novel closed-form expressions for the exact HO-aware rate outage probability and ergodic capacity in a large-scale network with interference. Then, we formulate a traffic flow maximization problem to optimize the speed of CAVs and deployment density of BSs with HO-aware rate constraints and collision avoidance constraints. Our numerical results validate the closed-form analytical expressions, extract useful insights about the optimal speed and BS density, and highlight the key trade-offs between the HO-aware data rates and CAV traffic flow.

Mixed traffic composed of human-driven vehicles (HDVs) and CAVs will exist for an extended period before connected and autonomous vehicles (CAVs) are fully employed on the road. There is a consensus that dense fog can cause serious traffic accidents and reduce traffic efficiency. In order to enhance the safety, mobility, and efficiency of highway networks in adverse weather conditions, it is necessary to explore the characteristics of mixed traffic. Therefore, we develop a novel cellular automata model for mixed traffic considering the limited visual distance and exploring the influence of visibility levels and CAV market penetration on traffic efficiency. We design acceleration, deceleration, and randomization rules for different car-following scenes. For lane-changing, considering the interaction of CAVs and surrounding vehicles, we introduce game theory (GT) to lane-changing policies for CAVs. This paper presents the following main findings. In reduced visibility conditions, the introduction of CAVs is beneficial to improve mixed traffic efficiency on metrics such as free-flow speed and traffic capacity (e.g., 100% CAVs could increase the traffic capacity up to around 182% in environments of dense fog). In addition, the critical density increases as the proportion of CAVs increases, which is more pronounced in conditions of dense fog according to the simulation results. In addition, we compared the proposed GT-based lane-changing strategy to the traditional STCA lane-changing strategy. The results showed that the average speed is significantly improved under the proposed lane-changing strategy. The model presented in this paper can evaluate the overall performance and provide a reference for future management and control of mixed traffic flow in fog conditions.

An effective intelligent transportation system is a core part of modern smart city. The Internet of Things and vehicular communication technologies facilitate rapid development of connected and autonomous vehicles (CAVs). While most studies focus on standalone CAV technologies, collective CAV control has much potential. With the connectivity and automation of CAVs, we can employ dynamic lane reversal (DLR) to optimize the travel schedules of CAVs for performance enhancement. In this paper, we propose the dynamic lane reversal-traffic scheduling management (DLR-TSM) scheme for CAVs. The system collects the travel requests from CAVs and determines their optimal schedules and routes over dynamically reversible lanes. We formulate the routing and scheduling problem on DLR as an integer linear program. To address the scaling effect, an algorithm based on alternating direction method of multipliers is designed to solve the problem in a distributed manner. We extensively evaluate the DLR-TSM and the distributed algorithm with real-world transportation data. The simulation results show that the DLR-TSM can significantly improve the travel times of CAVs and the distributed algorithm can dramatically reduce the required computational time.

Effect of connected and autonomous vehicles on traffic flow at a bidirectional road

Joint routing and pricing control in congested mixed autonomy networks

The macroscopic evaluation of road traffic characteristics is vital as they will alter significantly with the emergence of Connected and Autonomous Vehicles (CAVs). The macroscopic second-order traffic flow models are emerging as essential tools to observe the evolutionary dynamics of traffic flow. These models are significantly improved to describe the dynamics of traffic flow in view of Human-Driven Vehicles (HDVs). However, proficient macroscopic second-order traffic flow models are indispensable in comprehending and elucidating the descriptions of traffic flow dynamics, particularly in the view of evolving CAVs. Therefore, we propose a macroscopic second-order traffic model that distinguishes CAVs from HDVs by meticulously considering their distinct driving characteristics. The proposed model incorporates lengthways between the vehicles, and the reaction time of the CAVs, and is evaluated on a 20,000 m ring road. The findings show that with smaller lengthways and reaction times of CAVs, traffic evolves quickly during transitions. An increase in average traffic velocity is observed as the reaction time is significantly small. It has been demonstrated that CAVs can significantly limit variations in velocity resulting in smoother flow operations.

Optimal Control for Connected and Autonomous Vehicles at Signal-Free Intersections

The Connected and Autonomous Vehicle (CAV) is an emerging mobility technology that may hold a paradigm-changing potential for the future of transport policy and planning. Despite a wealth of likely benefits that have made their eventual launch inescapable, CAVs may also be a source of unprecedented disruption for tomorrow's travel eco-systems because of their vulnerability to cyber-threats, hacking and misinformation. CAVs manipulated by users, traffic controllers or third parties may act in deceitful ways. This scene-setting work introduces the deceitful CAV, a vehicle that operates in a deceitful manner towards routing and control functionality for ‘selfish’ or malicious purposes and contextualises its diverse expressions and dimensions. It specifically offers a systematic taxonomy of eight distinctive deceitful behaviours namely: suppression/camouflage, overloading, mistake, substitution, target conditioning, repackaging capability signatures, amplification and reinforcing impression. These as exemplified by their most common attack forms (i.e., starvation, denial-of-service, session hijacking, man-in-the-middle, poisoning, masquerading, flooding and spoofing) are then benchmarked against five key dimensions referring to time frame (short to long duration), engagement (localised to systemic), urban traffic controller infrastructure (single to multiple components), scale (low to high), and impact (low to high). We then suggest mitigation strategies to protect CAV technology against these dangers. These span from purely technological measures referring to the machine-centric triad of vehicles, communication, and control system including adversarial training, heuristic decision algorithms and weighted voting mechanisms to human factor measures that focus on education, training, awareness enhancement, licensing and legislation initiatives that will enable users and controllers to prevent, control or report deceitful activities.

The Internet of Things has already demonstrated clear benefits when applied in many areas. In connected and autonomous vehicles (CAV), IoT data can help the autonomous systems make better decisions for safer and more secure transportation. For example, different IoT data sources can extend CAV's risk awareness, while the incoming data can update these risks in real-time for faster reactions that may mitigate possible damages. However, the current state of the art CAV research has not addressed this matter well enough. This paper proposes a profile-driven approach to manage IoT data in the context of CAV systems through a dynamic risk management framework. Unlike the current inflexible risk assessment strategies, the framework encourages more flexible investigation of risks through different risk profiles, each representing risk knowledge through a set of risk input considerations, assessment methods and optimal reaction strategies. As the risks change frequently with time and location, there will be no single profile that can cover all the risks that CAVs face on the road. The uses of different risk profiles, therefore can help interested parties to better understand the risks and adapt to various situations appropriately. Our framework includes the effective management of IoT data sources to enable the run-time risk assessment. We also describe a case study of using the proposed framework to manage the risks for the POD being developed in the Innovate UK-funded CAPRI project.
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Recently, the Internet of vehicles (IoV) and connected and autonomous vehicles (CAVs) have become research hotspots. The accuracy and efficiency of artificial intelligence (AI) models are crucial for CAVs to make decisions automatically. Intelligence networking enables each CAV to train appropriate models locally with the help of other CAVs' intelligence and make up for the lack of experience and computing power of a single-vehicle. However, intelligence is distributed across diverse geo-locations in the intelligence networking paradigm, which calls for accurate, efficient, and lightweight intelligence discovery mechanisms. In this article, we propose a non-fungible token (NFT)-based distributed intelligence networking scheme (NDIN) for CAVs. We use NFT to tokenize intelligence and efficiently describe intelligence from multiple aspects through meta-data, facilitating applications to better understand and search for intelligence in complex and trust-lacking IoV. We present the architecture, modules, and core mechanism of NDIN. Then, we formulate the essential problem as a discrete Markov decision process (MDP) and adopt the quantum-inspired reinforcement learning (QRL) algorithm to find the optimal policy. Also, the convergence rate and performance compared with existing schemes are evaluated. Finally, we discuss several related challenges and opportunities.