Cooperative Vehicle Research Articles

Cooperative Driving of Automated Vehicles Using B-Splines for Trajectory Planning

Vehicle automation is used to increase traffic throughput and road safety. Automated vehicles should be highly versatile to navigate on both highways and urban roads. To do so, fully automated autonomous vehicles often adopt motion planners to ensure collision-free and comfortable trajectories. In contrast, in cooperative driving, feedback control is often used to achieve string-stable vehicle-following at very short inter-vehicle distances. In this work, the gap between cooperative vehicle following and trajectory planning is bridged, by using a trajectory planner to generate cooperative trajectories, such that the versatility of an autonomous vehicle is retained, but cooperation is included. The presented method uses B-splines to generate cooperative trajectories. Simulations are used to demonstrate that even under large communication delays, string stable behaviour can be achieved. The use of parametrized trajectories in the form of B-splines ensures only a small number of parameters needs to be communicated between vehicles.

Comprehensive performance analysis and optimal design of smart light pole for cooperative vehicle infrastructure system

Abstract On the basis of the finite element theory, in this study, a theoretical design and simulation practice to carry out steady-state static analysis, structural dynamics analysis, and electromagnetic interferencecharacteristics analysis on smart light poles, is combined. On this basis, a design of modular weight reduction and cost reduction was proposed, which realized the multi-objective parameter optimization of the smart light pole. The simulation results show that the mass of the optimized light pole can be reduced by 14.2%, and the material cost can be reduced by 14.7%. At the same time, the comprehensive performance of the optimized smart light pole can still meet the design requirements of the industry standards. The research results provide a reference for the lightweight design and the comprehensive analysis of the smart light pole in the future.

Research on Performance and Function Testing of V2X in a Closed Test Field

The V2X and cooperative vehicle infrastructure system (CVIS), which leverage the efficient information interactions through V2V, V2I, V2P, and V2N, are known as the advanced and effective technology in reducing traffic accidents and improving traffic efficiency. The complex technical characteristics of V2X and highly reliable service demand of typical V2X applications call for the test needs before the large-scale deployment of V2X. It indicates that the performance and function of V2X devices should be systematically tested and evaluated in extreme and boundary conditions of driving and communication environments before being broadly deployed and applied in infrastructures. Motivated by the previously mentioned needs, a performance and function testing scheme of V2X in a closed test field is studied. According to the analytical viewpoint from the physical layer and MAC layer, the proposed research systematically analyses the technical differences of DSRC and LTE-V, which are two typical V2X protocols, in terms of vehicle speed, communication distance, and channel adaptability. Based on the critical practical test needs from the analytical study, a function and performance test system of V2X specifically for the closed test field is proposed. The performance and typical application effectiveness in intersection environment of DSRC and LTE-V are evaluated. The limitation and proposed improvement strategies of these V2X protocols are analytically discussed.

Human-Robot Cooperation for Autonomous Vehicles and Human Drivers: Challenges and Solutions

Connected and automated vehicles (CAVs) are becoming increasingly feasible, and these vehicles are expected to enhance road safety, traffic throughput, energy efficiency, and human comfort, especially at road intersections and for lane changing, merging, and unprotected turning maneuvers. However, there is likely to be a long transition period before human-driven vehicles are completely replaced by such automated vehicles. To leverage the autonomous driving technologies on public roads, we have to design and develop safe interaction and cooperation for mixed traffic environments, where human-driven and autonomous vehicles cooperate with one another in order to avoid vehicle collisions and possible deadlocks. In this article, we study the requirements and challenges for such safe cooperation of autonomous vehicles and human drivers, and review three prospective components: road infrastructure, vehicular communications, and online recognition and prediction. First, road infrastructure contributes to improving CAV capabilities for safe cooperation as a centralized coordinator. In addition, vehicle-to-vehicle communications might be key to safe cooperation in a decentralized manner that can guarantee road safety for multiple connected vehicles. Online motion prediction and real-time gesture recognition are also essential to develop a safe cooperation system in a decentralized manner with non-connected vehicles. These three approaches are not exclusive, and CAVs may complementarily use them in practice. Finally, we discuss the feasibility and limitations of each approach and conclude with future research directions.

Tracking Model Predictive Control for Docking Maneuvers of a CubeSat with a Big Spacecraft

The release and retrieval of a CubeSat from a big spacecraft is useful for the external inspection and monitoring of the big spacecraft. However, docking maneuvers during the retrieval are challenging since safety constraints and high performance must be achieved, considering the small dimensions and the actual small satellites technology. The trajectory control is crucial to have a soft, accurate, quick, and propellant saving docking. The present paper deals with the design of a tracking model predictive controller (TMPC) tuned to achieve the stringent docking requirements for the retrieval of a CubeSat within the cargo bay of a large cooperative vehicle. The performance of the TMPC is verified using a complex model that includes non-linearities, uncertainties of the CubeSat parameters, and environmental disturbances. Moreover, 300 Monte Carlo runs demonstrate the robustness of the TMPC solution.

Modeling and Gender Difference Analysis of Acceptance of Cooperative Vehicle Infrastructure System

Modeling and Gender Difference Analysis of Acceptance of Cooperative Vehicle Infrastructure System

Speed Guidance Strategy for Buses Run on Overlapping Route Segments Under Cooperative Vehicle Infrastructure

Speed Guidance Strategy for Buses Run on Overlapping Route Segments Under Cooperative Vehicle Infrastructure

Driver's Acceptance of Cooperative Vehicle Infrastructure System Based on Extended TAM

Driver's Acceptance of Cooperative Vehicle Infrastructure System Based on Extended TAM

Simulation of mixed traffic with cooperative lane changes

AbstractThis study proposes a mixed traffic simulation framework that integrates vehicle car‐following (CF) and lane‐changing (LC) with connected and automated vehicles (CAVs) of different cooperation behaviors. This framework is centered at a CAV LC model incorporated with CF dynamics in mixed traffic. The model was calibrated and validated using data collected from small‐scale field experiments in a previous study. To demonstrate a large‐scale application of this framework, PTV Vissim was used to implement the framework on a segment of Interstate 75 highway. Sensitivity analyses were conducted to investigate the impacts of key parameters on traffic mobility and stability performance. The results show that traffic performance degraded as the traffic demand and vehicle diverging rate increased. As the CAV penetration rate increased, traffic performance fluctuated when CAVs were more conservative. As the CAV cooperation rate, incentive criterion threshold, and incentive criterion bias increased, mobility and stability performance first improved and then degraded. When CAV platooning was considered, traffic performance was enhanced. These findings shed light on mixed traffic management from the perspectives of both transportation operators (e.g., facilities and policies to promote vehicle cooperation) and automakers (e.g., tuning parameters in their LC models) to achieve the best traffic performance.

CSLPPS: Concerted Silence-Based Location Privacy Preserving Scheme for Internet of Vehicles

The Internet of Vehicles (IoV) connects vehicles together and also to other internet of things components such as sensors and smart devices. The huge amount of data generated by these components is stored in the cloud, so does the services offered which are not limited to safety applications but also to infotainment where most of these services use the position and identity of the vehicle. Due to these requirements, the privacy of road users is at threat. Thus, preserving it is highly important as it is directly related to their safety. If a specific user is targeted on the road by an attacker who knows his/her identity and real-time location, the consequences can vary from simple stalking to assassination. This paper proposes a new solution called Concerted Silence-based Location Privacy Preserving Scheme for Internet of Vehicles (CSLPPS) that ensures the anonymous and unlinkable participation in the IoV location-based services and vehicular networks safety applications. This scheme relies on the synchronization of the identifier change between the cooperative vehicles to enter a silent period simultaneously before continuing their cyber-activity with new identifiers. The simulation and study of performance proved efficiency of CSLPPS against a modeled global passive attacker and when compared to state-of-art solution.

COMAP: A SYNTHETIC DATASET FOR COLLECTIVE MULTI-AGENT PERCEPTION OF AUTONOMOUS DRIVING

Abstract. Collective perception of connected vehicles can sufficiently increase the safety and reliability of autonomous driving by sharing perception information. However, collecting real experimental data for such scenarios is extremely expensive. Therefore, we built a computational efficient co-simulation synthetic data generator through CARLA and SUMO simulators. The simulated data contain image and point cloud data as well as ground truth for object detection and semantic segmentation tasks. To verify the superior performance gain of collective perception over single-vehicle perception, we conducted experiments of vehicle detection, which is one of the most important perception tasks for autonomous driving, on this data set. A 3D object detector and a Bird’s Eye View (BEV) detector are trained and then test with different configurations of the number of cooperative vehicles and vehicle communication ranges. The experiment results showed that collective perception can not only dramatically increase the overall mean detection accuracy but also the localization accuracy of detected bounding boxes. Besides, a vehicle detection comparison experiment showed that the detection performance drop caused by sensor observation noise can be canceled out by redundant information collected by multiple vehicles.

Fundamental Analysis of Vehicular Light Communications and the Mitigation of Sunlight Noise

Intelligent transport systems (ITS) rely upon the connectivity, cooperation and automation of vehicles aimed at the improvement of safety and efficiency of the transport system. Connectivity, which is a key component for the practical implementation of vehicular light communications (VeLC) systems in ITS, must be carefully studied prior to design and implementation. In this paper, we carry out a performance evaluation study on the use of different vehicle taillights (TLs) as the transmitters in a VeLC system. We show that, the transmission coverage field of view and the link span depend on TLs illumination patterns and the transmit power levels, respectively, which fail to meet the typical communication distances in vehicular environments. This paper proposes an infrared-based VeLC system to meet the transmission range in daytimes under Sunlight noise. We show that, at the forward error correction bit error rate limit of 3.8 ×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , the communication distances of the proposed link are 63, 72, and > 89 m compared with 4.5, 5.4 and 6.3 m for BMW's vehicle TL at data rates of 10, 6, and 2 Mbps, respectively.

Distributed Cooperative Driving in Multi-Intersection Road Networks

Cooperative driving at isolated intersections attracted great interest and had been well discussed in recent years. However, cooperative driving in multi-intersection road networks remains to be further investigated, because many algorithms for isolated intersection cannot be directly adopted for road networks. In this paper, we propose a distributed strategy to appropriately decompose the problem into small-scale sub-problems that address vehicle cooperation within limited temporal-spatial areas and meanwhile assure appropriate coordination between adjacent areas by specially designed information exchange. Simulation results demonstrate the efficiency-complexity balanced advantage of the proposed strategy under various traffic demand settings.

Research on Urban Traffic Active Control in Cooperative Vehicle Infrastructure

Urban intersection control mainly undertakes two tasks: traffic safety and traffic efficiency. Traditional intersection control models and methods have been insufficient in improving traffic efficiency, which is composed of the increase in traffic demand and the complexity of demand at present. In this paper, we propose a novel model and method called ATCM, which is based on the advanced technology of cooperative vehicle infrastructure. In this paper, a novel active traffic control model (ATCM) is proposed, which is based on the advanced technology of cooperative vehicle infrastructure. ATCM increases the intersection control model variables from the traditional two dimensions to five dimensions. It reshapes intersection control from the perspective of road designers and managers, so it can achieve more flexible and efficient traffic control. To this end, a multivariable active traffic control model is constructed, which includes road speed, lane control, sequence, phase, and green light time; a D-double layer optimization method is designed for this model. The first part of this optimization method combines speed control and dynamic phase sequence control. The second part is realized by the combination of lane control and dynamic phase sequence control. By conducting comprehensive experiments, the results demonstrate that the proposed approach is more flexible and efficient than traditional methods.

Autonomous Integrity Monitoring for Relative Navigation of Multiple Unmanned Aerial Vehicles

Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely used due to its worldwide coverage and simplicity in relative navigation. However, the observations of GNSS are vulnerable to different kinds of faults arising from transmission degradation, ionospheric scintillations, multipath, spoofing, and many other factors. In an effort to improve the reliability of multi-UAV relative navigation, an autonomous integrity monitoring method is proposed with a fusion of double differenced GNSS pseudoranges and Ultra Wide Band (UWB) ranging units. Specifically, the proposed method is designed to detect and exclude the fault observations effectively through a consistency check algorithm in the relative positioning system of the UAVs. Additionally, the protection level for multi-UAV relative navigation is estimated to evaluate whether the performance meets the formation flight and collision avoidance requirements. Simulated experiments derived from the real data are designed to verify the effectiveness of the proposed method in autonomous integrity monitoring for multi-UAV relative navigation.

Blockchain-Based Solutions for UAV-Assisted Connected Vehicle Networks in Smart Cities: A Review, Open Issues, and Future Perspectives

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

Autonomous Last-Mile Delivery Based on the Cooperation of Multiple Heterogeneous Unmanned Ground Vehicles

With the development of e-commerce, the last-mile delivery has become a significant part of customers’ shopping experience. In this paper, an autonomous last-mile delivery method using multiple unmanned ground vehicles is investigated. Being a smart logistics service, it provides a promising solution to reduce the delivery cost, improve efficiency, and avoid the spread of airborne diseases, such as SARS and COVID-19. By using a cooperation strategy with multiple heterogeneous robots, contactless parcel delivery can be carried out within apartment complexes efficiently. In this paper, the last-mile delivery with heterogeneous UGVs is formulated as an optimization problem aimed at minimizing the maximum makespan to complete all tasks. Then, a heuristic algorithm combining the Floyd’s algorithm and PSO algorithm is proposed for task assignment and path planning. This algorithm is further realized in a distributed scheme, with all robots in a swarm working together to obtain the best task schedule. A good solution with an optimized makespan is achieved by considering the constraints of various robots in terms of speed and payload. Simulations and experiments are carried out and the obtained results confirm the validity and applicability of the developed approaches.

First Observations about Response Times and Connectivity in a Vehicles Platooning Experiment

A key question about cooperative vehicle longitudinal control is reactivity, which determines the future of road safety, and capacity. In adaptive cruise control (ACC), the controller adapts the speed of the vehicle to its immediate leader’s speed whereas, in the cooperative version (CACC), connectivity between the platoon equipped vehicles reduces their response times. The USDoT Cooperative Automated Research Mobility Applications (CARMA) platform provides data for platooning experiments involving ACC and CACC vehicles. We measure ACC response times (mean = 2.78 seconds) larger than for human-driven cars. We study response times inside CACC platoons showing that connectivity is not always effective.

An Intelligent Ground-Air Cooperative Navigation Framework Based on Visual-Aided Method in Indoor Environments

In the future, heterogeneous robots are expected to perform more complex tasks in a cooperative manner, and the onboard navigation system is required to be capable of working safely and effectively in the area where GNSS signal is weak or even could not be received. To demonstrate this concept, we have developed a cooperative navigation system by the use of Ground-Aerial Vehicle Cooperation. The key innovations of the development lie in the following aspects: (1) a local scalable self-organizing network is constructed for data interaction between a small UAV and a reusable ground robot; (2) a new navigation framework is proposed to achieve visual simultaneous localization and mapping (SLAM) where carrying capacity of both the ground vehicle and UAV are systematically considered; (3) an octomap-based 3D environment reconstruction method is developed to achieve map pre-establishment in complex navigation environments, and the classic ORB-SLAM2 system is improved to be adaptive to actual environment exploration and perception. In-door flight experiments demonstrate the effectiveness of the proposed solution. More interestingly, by implementing a centroid tracking algorithm, the cooperative system is further capable of tracking a man moving in indoor environments.

Undersampled Differential Phase Shift On–Off Keying for Visible Light Vehicle-to-Vehicle Communication

An important development direction for the future of the automotive industry is connected and cooperative vehicles. Some functionalities in traffic need the cars to communicate with each other. In platooning, multiple cars driving in succession reduce the distances between them to drive in the slipstream of each other to reduce drag, energy consumption, emissions, and the probability of traffic jams. The car in front controls the car behind remotely, so all cars in the platoon can accelerate and decelerate simultaneously. In this paper, a system for vehicle-to-vehicle communication is proposed using modulated taillights for transmission and an off-the-shelf camera with CMOS image sensor for reception. An Undersampled Differential Phase Shift On–Off Keying modulation method is used to transmit data. With a frame sampling rate of 30 FPS and two individually modulated taillights, a raw data transmission rate of up to 60 bits per second is possible. Of course, such a slow communication channel is not applicable for time-sensitive data transmission. However, the big benefit of this system is that the identity of the sender of the message can be verified, because it is visible in the captured camera image. Thus, this channel can be used to establish a secure and fast connection in another channel, e.g., via 5G or 802.11p, by sending a verification key or the fingerprint of a public key. The focus of this paper is to optimize the raw data transmission of the proposed system, to make it applicable in traffic and to reduce the bit error rate. An improved modulation mode with smoother phase shifts is used that can reduce the visible flickering when data is transmitted. By additionally adjusting the pulse width ratio of the modulation signal and by analyzing the impact of synchronization offsets between transmitter and receiver, major improvements of the bit error rate (BER) are possible. In previously published research, such a system without the mentioned adjustments was able to transmit data with a BER of 3.46%. Experiments showed that with those adjustments a BER of 0.48% can be achieved, which means 86% of the bit errors are prevented.