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This paper discusses scalable cooperative maneuvering using conflict analysis, where conflicts are managed between multiple vehicles in mixed-autonomy environments. Two different classes of cooperation, enabled by vehicle-to-everything (V2X) communication, are considered–status sharing and intent sharing. In status sharing, connected vehicles share their current states, such as position and velocity, while in intent sharing the information about vehicles’ future trajectories is exchanged. The scalability of conflict analysis is studied through traffic scenarios where an ego vehicle interacts with multiple remote vehicles in a safety- and time-critical manner. We show thatpairwiseconflict analysis, which decomposes a large-scale conflict management problem to multiple sequentially solvable smaller-scale problems, is a key component of scalability. Conflict analysis, while being scaled up to more complex traffic scenarios, preserves the efficient consideration of different types of V2X information, time delay effects, and flexible control design. These results are demonstrated using simulations with real traffic data.

In this study, we propose a novel negotiation-based cooperative maneuvering strategy to assist connected automated vehicles (CAVs) in resolving conflicts under different traffic scenarios. We introduce conflict charts to determine when negotiation is necessary, along with a request and response protocol to facilitate traffic conflict resolution. Additionally, we propose an easy-to-implement controller that allows CAVs to resolve conflicts based on the agreement reached through negotiation. Simulation results using real vehicle data are used to demonstrate that the proposed negotiation protocol helps to ensure safety while improving time efficiency compared to cooperations that rely on other communication strategies.

In this paper, we propose models with explicit trainable delays for learning teleoperated driving (ToD) behavior from limited vehicle trajectory data. The data-driven model is integrated with physics-based nonlinear vehicle dynamics and formulated as a neural delay differential equation (NDDE). The model can be analyzed using the same tools as developed for classical delay differential equations. The physics-based nonlinearity built into the data-driven model reduces the model complexity, enables training with limited data, and provides good generalizations. An overall latency in the loop is learned and a generic steering controller that characterizes the remote operator is identified at the same time through the training process. This information could be used to evaluate the performance of ToD in the presence of communication latency. We provide examples of learning from simulation data generated by a kinematic vehicle model and from experimental data generated by a human operator driving in a high-fidelity simulation environment. The same data-driven model and training algorithm is used in both cases, which demonstrates the generalizability of the proposed approach.

We explore the advantages of using deep learning-based intent sharing for platooning of connected automated vehicles (CAVs). Unlike traditional platooning algorithms that rely on status-sharing - exchanging current position, speed, and acceleration-our approach focuses on intent-sharing, where CAVs share predicted future trajectories with other CAVs. We introduce a deep learning model to generate the intent for each CAV and integrate it into a receding horizon control framework. Our approach aims to minimize spacing errors with the leading vehicle while improving energy efficiency and maintaining string stability. Through microscopic simulations using real-world highway data, we demonstrate that our intent messages significantly enhance energy efficiency compared to conventional status and intent-based platooning algorithms. Moreover, we show that this improvement is particularly pronounced when reducing the frequency of intent message transmission.

The nonlinear dynamics of vehicles on a virtual ring is investigated. A vehicle chain is considered where a connected automated vehicle (CAV) driving at the head of the chain receives the state of a connected human-driven vehicle (CHV) at the tail. The controller of the CAV is constructed in a way that the CHV is projected in front of it; this closes a virtual ring. We construct the corresponding mathematical model and analyze the effect of nonlinearities with numerical continuation. Then, we present real car experiments with two CHVs and one CAV. Both the theoretical results and the experiments show bistable behavior for certain control parameters. The results provide an essential support for parameter tuning during the control design of CAVs.

This paper examines the critical role of intent-sharing in enabling effective maneuver coordination for connected and automated vehicles (CAVs). Successful maneuver coordinations require vehicles to accurately know other vehicles' driving intentions. Intent-sharing can be achieved by the remote vehicles directly communicating their plans with the ego vehicle, as opposed to the ego vehicle predicting the trajectory on the remote vehicles’ behalf. In this paper, we investigate the potential of intent-sharing on maneuver coordination effectiveness by quantifying the percentage of successful coordinations. We analyze the potential of intent-sharing by comparing its effectiveness for coordinated lane changes in a highway scenario with the effectiveness of a trajectory prediction method based on current kinematic data. Our analysis demonstrates in two scenarios substantial improvements in maneuver coordination when CAVs have direct access to the nearby vehicles’ driving intentions through intent sharing. These findings highlight the importance of including intent-sharing in the maneuver coordination protocol.

This study investigates the integration of intent-sharing within a multi-agent platooning system using a reinforcement learning-based control approach. The method enables vehicles to exchange both their current state information and predictions of future speed and acceleration, thereby enhancing decision-making and coordination. By incorporating intent-sharing, the system effectively reduces spacing errors and maintains optimal inter-vehicle spacing, which results in smoother acceleration and deceleration profiles; critical factors for passenger comfort, safety, and platoon string stability. Experimental data confirm that intent-sharing significantly improves string stability, safety, comfort, and gap-keeping accuracy in connected vehicle platooning.

In this paper, we demonstrate how the network latency, the longitudinal velocity and the path curvature affect performance of the teleoperated driving (ToD). The performance of a ToD system is studied analytically through stability analysis of a dimensionless vehicle dynamics model with a scaled delay, which integrates the end-to-end (E2E) latency and the longitudinal velocity of the vehicle. We also establish a numerical simulation framework for ToD while incorporating a stochastic latency in the control loop arising from vehicle-to-network-to-vehicle (V2N2V) communication through a wireless network. The stochasticity of the latency mostly comes from the network scalability challenges to support high video bitrates, which also leads to packet drops. We provide simulation results of teleoperating a vehicle in a realistic parking lot scenario and demonstrate the effects of speed, curvature and stochastic latency on the maneuver performance.

Negotiation is a class of cooperation enabled by vehicle-to-everything (V2X) communication, which involves the exchange of maneuver requests and responses between road users. In this paper, we develop criteria for request initiation and response generation under a unified conflict analysis framework. This leads to guaranteed maneuver feasibility in request and response that satisfy user-based behavior preferences. We implement negotiation via commercially available V2X devices, and experimentally evaluate the benefits of negotiation in conflict resolution. We demonstrate that negotiation can significantly benefit time efficiency of maneuvers while ensuring safety, compared to lower levels of cooperation such as status-sharing and intent-sharing. These benefits and their degradation under communication delays are quantified.

Teleoperated driving (ToD) enables the remote driving or control of vehicles. For this purpose, vehicles must transmit video feeds to the ToD control center so that the remote operator is fully aware of the driving conditions and can safely control the vehicle. 5G (and beyond) networks are fundamental for the deployment of ToD as they can provide the low latency, reliable and broadband connection necessary to connect the vehicle and ToD control center. However, it is unclear whether common 5G network architectures and configurations are well-suited to support the simultaneous teleoperation of multiple vehicles with demanding uplink bandwidth, as current networks are mainly configured to support mobile broadband services. This paper demonstrates that MEC or edge-based 5G networks are better suited to support and scale the ToD service than centralized networks, and quantifies the bandwidth required to simultaneously teleoperate multiple vehicles under various 5G network architectures and configurations, including different duplexing modes and TDD frame structures. Finally, the study shows that the configuration of the control channels can help mitigate the impact that the processing time of the video feeds has on the capacity to support and scale the ToD service.

Connected and automated vehicles can leverage V2X communications to coordinate their maneuvers. Maneuver coordination is expected to improve traffic efficiency and safety, but the design of maneuver coordination is a challenging task in complex traffic scenarios, as maneuvers affect not only the involved vehicles but also nearby traffic. This study introduces a reference state machine for the design of maneuver coordination. Furthermore, we identify and analyze the challenges that maneuver coordination may encounter. We quantify the relevance of each challenge and propose a set of countermeasures to enhance the robustness and effectiveness of maneuver coordination.

In this paper we investigate the benefits of intent-sharing for platooning of connected automated vehicles (CAVs). Intent sharing is a new class of cooperation for CAVs, where each CAV share their planned future trajectory with other CAVs. In contrast, most existing platooning algorithms, are based on status-sharing cooperation, where CAVs only exchange their current position, speed, and acceleration. We present a receding horizon control framework for CAVs that utilizes intent messages, and minimizes spacing error with respect to the leading vehicle, while considering comfort and string stability. We compare our intent-sharing approach to a baseline CACC algorithm through microscopic simulations based on real-world highway data. Our simulations reveal significant improvements in energy efficiency and road capacity compared to the baseline CACC algorithm.

Intent sharing is a class of cooperation enabled by vehicle-to-everything (V2X) communication, which allows for information exchange between road users about their intended future behaviors. In this paper, we propose a generalized representation of vehicles’ motion intent from a dynamical systems viewpoint. Based on this, we extend the framework of conflict analysis such that intent information can be interpreted in real time to assist the decision-making of intent-receiving vehicles and ensure conflict-free maneuvers. We create intent messages using commercially available V2X radios, and demonstrate experimentally the benefits of sharing intent in cooperative maneuvering. Experiments are performed on a test track where intent-based on-board decision assistance is provided to human drivers in merge scenarios. The experimental results reveal significant benefits of intent sharing in enhancing vehicle safety and time efficiency. Furthermore, we test intent messages on public roads and evaluate the performance in terms of packet delivery ratio. The data collected on public highways are fed into numerical simulations to investigate the effects of intent transmission conditions on conflict resolution.

Intent sharing is an emerging type of vehicle-to-everything (V2X) communication where vehicles share information about their intended future trajectories. In this study, we implement intent sharing via commercially available V2X devices, and experimentally demonstrate its benefits in resolving conflicts arising in cooperative maneuvering. An extended framework of conflict analysis is used to provide decision-making assistance via on-board warnings to a human-driven vehicle in highway merge scenario. We show that intent information can significantly benefit safety and time efficiency. Using the experimental data, we also evaluate the effects of communication conditions (e.g., sending rate and intent horizon) on the gained benefits.

Teleoperated driving (ToD) can support autonomous driving under complex or unexpected traffic scenarios that an autonomous vehicle may not understand or be able to handle. In ToD, autonomous vehicles transmit video feeds and perception data to the remote control center. The operator uses this data to understand the driving environment and remotely control the vehicle that can take over the control once the scenario is resolved. ToD requires reliable and low latency communications between the vehicle and the ToD control center. This study analyzes the feasibility to support ToD with 5G networks. The study demonstrates that the feasibility strongly depends on the bandwidth and the Time Division Duplexing (TDD) frame structure that conditions how the bandwidth is distributed between uplink and downlink transmissions. The study also shows that scaling the number of 5G-supported ToD vehicles requires the vehicles to reduce the video bitrates. The study also shows that traditional centralized 5G network deployments may be challenged by some of the most stringent ToD latency requirements due to the latency introduced by the Internet connection to the ToD control center.

In this paper, we showcase a framework for cooperative mixed traffic platooning that allows the platooning vehicles to realize multiple benefits from using vehicle-to-everything (V2X) communications and advanced controls on urban arterial roads. A mixed traffic platoon, in general, can be formulated by a lead and ego connected automated vehicles (CAVs) with one or more unconnected human-driven vehicles (UHVs) in between. As this platoon approaches an intersection, the lead vehicle uses signal phase and timing (SPaT) messages from the connected intersection to optimize its trajectory for travel time and energy efficiency as it passes through the intersection. These benefits carry over to the UHVs and the ego vehicle as they follow the lead vehicle. The ego vehicle then uses information from the lead vehicle received through basic safety messages (BSMs) to further optimize its safety, driving comfort, and energy consumption. This is accomplished by the recently designed cooperative adaptive cruise control with unconnected vehicles (CACCu). The performance benefits of our framework are proven and demonstrated by simulations using real-world platooning data from the CACC Field Operation Test (FOT) Dataset from the Netherlands.

In this paper, we extend the conflict analysis framework to resolve conflicts between multiple vehicles with different levels of automation, while utilizing status-sharing and intent-sharing enabled by vehicle-to-everything (V2X) communication. In status-sharing a connected vehicle shares its current state (e.g., position, velocity) with other connected vehicles, whereas in intent-sharing a vehicle shares information about its future trajectory (e.g., velocity bounds). Our conflict analysis framework uses reachability theory to interpret the information contained in status-sharing and intent-sharing messages through conflict charts. These charts enable real-time decision making and control of a connected automated vehicle interacting with multiple remote connected vehicles. Using numerical simulations and real highway traffic data, we demonstrate the effectiveness of the proposed conflict resolution strategies, and reveal the benefits of intent sharing in mixed-autonomy environments.

Cellular networks usually support non-safety-critical V2X services using Vehicle-to-Network (V2N) connections. However, the flexibility and capabilities of 5G have triggered interest in analyzing whether 5G could also support advanced V2X services using Vehicle-to-Network-to-Vehicle (V2N2V) connections instead of direct Vehicle-to-Vehicle (V2V) connections. V2N2V requires the integration of the 5G network with computing platforms for processing the V2X packets. The flexibility introduced by 5G facilitates the integration with multiple computing platforms such as Multi-access Edge Computing (MEC), edge cloud, shared data center or central cloud. This results in alternative 5G network deployments with the computing platform installed at different locations between the base station and the Internet. These deployments can have important technical implications for supporting V2X services. In this study, we analyze the impact of different 5G and computing platform deployments on the end-to-end (E2E) latency of V2N2V communications under multi-MNO (Mobile Network Operator) scenarios since vehicles may be served by different operators. We also identify which deployment strategies are more suitable to meet the latency requirements of V2X services for connected and automated driving.

In this paper, we use the Perceived Safety Analysis Framework (PSAF) to assess the awareness of vehicles performing an unprotected left turn at unsignalized intersections. PSAF is an analytical method developed to quantify the awareness of vehicles to surrounding safety-critical road users in traffic. We derive safety conditions for unprotected left turns using surrogate safety measures and right of way rules, and determine which road users are safety-critical to the left-turning ego vehicle. Then, we evaluate the Perceived Safety Error based on whether the ego vehicle can detect critical road users (CRUs) using sensors (such as camera or radar) and via vehicle-to-everything (V2X) communication. We demonstrate that for intersections with sparse traffic, vehicle-to-vehicle (V2V) communication may be insufficient for left-turning vehicles to get full awareness of CRUs, and vehicle-to-infrastructure (V2I) communication helps to eliminate awareness gaps.

5G and multi-access edge computing (MEC) are being considered to support V2X services demanding low latency and highly reliable communications using V2N2V (Vehicle-to-Network-to-Vehicles) communications instead of direct or sidelink V2V (Vehicle-to-Vehicle). Guaranteeing V2X service continuity using V2N2V is a challenging task in multi-Mobile Network Operator (MNO) deployments where vehicles are supported by different MNOs. MEC federations have been proposed to address some of these challenges. A MEC federation is a federated model of MEC systems enabling shared usage of MEC services and applications. Through MEC federations, vehicles can seamlessly access V2X applications independently of whether they are hosted on their MNO’s MEC, or on the MEC of a different (but federated) MNO. This paper presents the first study that analyses the impact of MEC federation on the end-to-end (E2E) latency when supporting V2X services using 5G V2N2V in multi-MNO scenarios. The paper also evaluates the feasibility to support the latency requirements of advanced V2X services in these scenarios, and the benefits introduced by MEC federation. This study considers the V2Xbased cooperative lane merge service as a case study.

Connected Automated Vehicles (CAVs) can utilize V2X communications to exchange their driving intentions and coordinate maneuvers. Previous studies have shown that maneuver coordination can improve traffic efficiency and safety. However, these studies focus on specific scenarios with a limited number of vehicles. Large-scale evaluations with complex interactions among vehicles are necessary to fully recognize the impact of maneuver coordination and to understand how to effectively design and conFigure maneuver coordination. This study progresses the state-of-the-art with a large-scale evaluation of lane change maneuver coordination in a highway scenario. We show how maneuver coordination impacts the traffic distribution and improves traffic fairness. Our study also highlights the need to consider safety when designing maneuver coordination and demonstrates that the coordination triggering conditions impact the execution of the coordination and their spatiotemporal distribution.

In this paper we investigate the effects of connected automated vehicles on traffic patterns. We first experimentally study traffic patterns using two connected human-driven vehicles, which are equipped with vehicle-to-vehicle (V2V) communication, and a connected automated vehicle, which is able to respond to V2V information and control its longitudinal motion. Our experimental results indicate the long-range feedback may benefit traffic flow and that car-following models with delay are able to replicate the experimental results. The data fitted models are used in simulations for a 100-car network to study traffic dynamics with partial penetration of connected automated vehicles.

In this paper, we propose an optimization-based strategy that utilizes vehicle-to-everything (V2X) communication in order to resolve conflicts between vehicles of different automation levels. The strategy consists of a decision checking mechanism and a control law to adjust the decision of an ego vehicle in a certain maneuver based on status update messages received from a remote vehicle involved in that maneuver. Using numerical simulations with real highway data, we demonstrate the proposed opportunistic strategy and show how it improves safety and maximizes the time efficiency of the ego vehicle. We also highlight the benefits of the strategy by comparing the results with an existing conservative strategy.

Connected and automated vehicles (CAVs) are expected to make use of Vehicle-to-Everything (V2X) communication to exchange sensor and trajectory data. Using this data, CAVs can coordinate their maneuvers for safer and more efficient driving. ETSI and SAE are currently working to define standards for maneuver coordination and cooperative driving. The current approach at ETSI is based on a distributed solution where vehicles use Vehicle-to-Vehicle (V2V) communication to exchange their planned and desired trajectories. This study evaluates the potential benefits of the ETSI Maneuver Coordination Service to improve the traffic speed using a unique simulation tool. To do so, we first evaluate the impact of maneuver coordination on the vehicles involved in a coordination process. We also evaluate the effects of maneuver coordination on the overall traffic compared to scenarios without coordination. Our study shows that maneuver coordination can yield significant benefits to traffic mobility, however these improvements are intimately linked to the surrounding traffic environment and the specifications of the coordinated maneuver. This highlights the need for more detailed studies on the design of maneuver coordination protocols that should consider the vehicular context when executing and configuring the coordination process.

5G offers high flexibility at the radio, transport and core networks to support various services of critical verticals such as connected and automated driving. At the Radio Access Network (RAN), 5G defines a New Radio (NR). 5G NR utilizes different subcarrier spacing, slot durations, modulations and channel coding schemes. This flexibility offers the possibility to support automotive services with different and demanding requirements, such as Advanced Driver-Assistance System (ADAS), cooperative driving, and remote driving. Previous studies showed that 5G NR can be configured to achieve latencies below 2 ms. However, existing studies are generally restricted to scenarios with a limited number of users and unlimited bandwidth. Therefore, it is important to analyze whether 5G NR can effectively support these services as the network scales under limited spectrum allocations. This study advances the current state of the art to demonstrate that the capability of 5G NR RAN to support advanced V2X services depends on the RAN configuration (subcarrier spacing, slot duration and error protection) and network load.

In this paper we investigate the problem of a vehicle merging to a main road while another vehicle is approaching on that road. We utilize conflict analysis to help the decision making and control for vehicles of different automation levels. We demonstrate that using vehicle-to-everything (V2X) communication, e.g., basic safety message (BSM), we are able to prevent conflict between the two vehicles. We design a longitudinal controller for the merging vehicle and show that V2X communication is also beneficial in improving the time efficiency of the merge. The results are demonstrated by performing simulations based on real highway data.