Cooperative Driving Research Articles

A Testing and Verification Approach to Tune Control Parameters of Cooperative Driving Automation Under False Data Injection Attacks

A Testing and Verification Approach to Tune Control Parameters of Cooperative Driving Automation Under False Data Injection Attacks

Cooperative Driving of Connected Autonomous Vehicles in Heterogeneous Mixed Traffic: A Game Theoretic Approach

Cooperative Driving of Connected Autonomous Vehicles in Heterogeneous Mixed Traffic: A Game Theoretic Approach

Data-Learning Game Output Regulation Approach for Human–Machine Cooperative Driving Toward Varied Drivers and Vehicles

Data-Learning Game Output Regulation Approach for Human–Machine Cooperative Driving Toward Varied Drivers and Vehicles

Evaluating Traffic-Calming-Based Urban Road Design Solutions Featuring Cooperative Driving Technologies in Energy Efficiency Transition for Smart Cities

Traffic-calming measures (TCMs) are non-invasive devices designed to improve road mobility and urban areas on a human scale. Despite their potential, they have been in use for a long time and now have to deal with the latest technological innovations in the automotive field, such as cooperative driving technologies (CDTs), to improve energy efficiency in cities. The goal of this study is to explore the safety and operational performances of TCMs featuring CDTs in urban areas. An urban-scale road network close to a seaside area in the City of Mazara del Vallo, Italy, was properly redesigned and simulated in AIMSUN to assess several design solutions, where connected and automated vehicles (CAVs) have been employed as a more energy-efficient public transportation system. Preliminarily, the fine-tuning process of model parameters included CAVs and human-operated vehicles (HOVs) flowing through the network up to saturation conditions. The safety of the planned solutions was tested by using surrogate measures. The micro-simulation approach allowed us to know in advance and compare the operational and safety performances of environmentally friendly solutions involving TCMs and CDTs. These results can also support urban road decision makers in pivoting urban-traffic-calming-based design solutions featuring cooperative driving technologies toward energy efficiency transitions for smart cities.

Multi-Agent Reinforcement Learning-Based Decision Making for Twin-Vehicles Cooperative Driving in Stochastic Dynamic Highway Environments

In the past decade, reinforcement learning (RL) has achieved encouraging results in autonomous driving, especially in well-structured and regulated highway environments. However, few researches pay attention to RL-based multiple-vehicles cooperative driving, which is much more challenging because of dynamic real-time interactions and transient scenarios. This paper proposes a Multi-Agent Reinforcement Learning (MARL) based twin-vehicles cooperative driving decision making method which achieves the generalization adaptation of the RL method in highly dynamic highway environments and enhances the flexibility and effectiveness of collaborative decision making system. The proposed fair cooperative MARL method pays equal attention to the individual intelligence and the cooperative performance, and employs a stable estimation method to reduce the propagation of overestimated joint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -values between agents. Thus, the twin-vehicles system strikes a balance between maintaining formation and free overtaking in dynamic highway environments, to intelligently adapt to different scenarios, such as heavy traffic, loose traffic, even some emergency. Targeted experiments show that our method has strong cooperative performance, also further increases the possibility of creating a harmonious driving environment.

Cyber Mobility Mirror: A Deep Learning-Based Real-World Object Perception Platform Using Roadside LiDAR

Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cyber Mobility Mirror</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CMM</i> ), a next-generation real-world object perception system for 3D object detection, tracking, localization, and reconstruction, to explore the potential of roadside sensors for enabling CDA in the real world. The CMM system consists of six main components: i) the data pre-processor to retrieve and preprocess the raw data; ii) the roadside 3D object detector to generate 3D detection results; iii) the multi-object tracker to identify detected objects; iv) the global locator to generate geo-localization information; v) the mobile-edge-cloud-based communicator to transmit perception information to equipped vehicles, and vi) the onboard advisor to reconstruct and display the real-time traffic conditions. An automatic perception evaluation approach is proposed to support the assessment of data-driven models without human-labeling requirements and a CMM field-operational system is deployed at a real-world intersection to assess the performance of the CMM. Results from field tests demonstrate that our CMM prototype system can achieve 96.99% precision and 83.62% recall for detection and 73.55% ID-recall for tracking. High-fidelity real-time traffic conditions (at the object level) can be geo-localized with a root-mean-square error (RMSE) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.69m$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.33m$</tex-math> </inline-formula> for lateral and longitudinal direction, respectively, and displayed on the GUI of the equipped vehicle with a frequency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3-4 Hz$</tex-math> </inline-formula> .

Toward Intelligent Connected E-Mobility: Energy-Aware Cooperative Driving With Deep Multiagent Reinforcement Learning

Toward Intelligent Connected E-Mobility: Energy-Aware Cooperative Driving With Deep Multiagent Reinforcement Learning

Cooperative Driving Automation Messaging Designs

Cooperative driving automation (CDA) has the potential to facilitate vehicle-to-everything (V2X), vehicle-to-infrastructure (V2I), and vehicle-to-vehicle (V2V) communications to improve traffic operations and safety. In this study, the researchers assessed several V2I and V2V message design configurations and characteristics. The study was conducted online through Web-conferencing sessions. The researchers presented 24 participants with different driving scenarios followed by relevant in-vehicle CDA message designs. The participants were then asked open- and closed-ended questions on message understanding, situational expectations, message effectiveness, and design preferences. The messages using V2I designs tended to be clearer and better understood than those using V2V designs. Certain colors (green, red) and designs (lines, numbers) tended to be more effective. This study provided insights into drivers’ perceptions of several CDA V2I and V2V message designs for specific scenarios. This study’s findings will help future researchers and practitioners select appropriate CDA messages to improve overall transportation operations and safety.

Multi-Technology Cooperative Driving: An Analysis Based on PLEXE

Cooperative Driving requires ultra-reliable communications, and it is now clear that no single technology will ever be able to satisfy such stringent requirements, if only because active jamming can kill (almost) any wireless technology. Cooperative driving with multiple communication technologies which complement each other opens new spaces for research and development, but also poses several challenges. The work we present tackles the fallback and recovery mechanisms that the longitudinal controlling system of a platoon of vehicles can implement as a distributed system with multiple communication interfaces. We present a protocol and procedure to correctly compute the safe transition between different controlling algorithms, down to autonomous (or manual) driving when no communication is possible. To empower the study, we also develop a new version of PLEXE, which is an integral part of this contribution as the only Open Source, free simulation tool that enables the study of such systems with a modular approach, and that we deem offers the community the possibility of boosting research in this field. The results we present demonstrate the feasibility of safe fallback, but also highlight that such complex systems require careful design choices, as naive approaches can lead to instabilities or even collisions, and that such design can only be done with appropriate in-silico experiments.

A Methodological Framework to Assess Road Infrastructure Safety and Performance Efficiency in the Transition toward Cooperative Driving

There is increasing interest in connected and automated vehicles (CAVs), since their implementation will transform the nature of transportation and promote social and economic change. Transition toward cooperative driving still requires the understanding of some key questions to assess the performances of CAVs and human-driven vehicles on roundabouts and to properly balance road safety and traffic efficiency requirements. In this view, this paper proposes a simulation-based methodological framework aiming to assess the presence of increasing proportions of CAVs on roundabouts operating at a high-capacity utilization level. A roundabout was identified in Palermo City, Italy, and built in Aimsun (version 20) to describe the stepwise methodology. The CAV-based curves of capacity by entry mechanism were developed and then used as target capacities. To calibrate the model parameters, the capacity curves were compared with the capacity data simulated by Aimsun. The impact on the safety and performance efficiency of a lane dedicated to CAVs was also examined using surrogate measures of safety. The paper ends with highlighting a general improvement with CAVs on roundabouts, and with providing some insights to assess the advantages of the automated and connected driving technologies in transitioning to smarter mobility.

Comparative Analysis of Cooperative Driving Strategies for CAVs at On-Ramps

This paper focuses on cooperative driving strategies at on-ramps and comprehensively compares the performance of five representative strategies. The simulation results show that the dynamic programming (DP)-based, the grouping-based, and the rule-based strategies perform well in computation time and traffic efficiency, so all three strategies are recommended for practical use. We also show that improving traffic efficiency is usually at the expense of fairness, but a better trade-off between them can be realized in the modified DP-based and rule-based strategies. All cooperative driving strategies compared in this study will be integrated into CAVSim (a simulation platform dedicated to CAVs) for the convenience of researchers and the community.

The OpenCDA Open-Source Ecosystem for Cooperative Driving Automation Research

Advances in Single-vehicle intelligence of automated driving has encountered great challenges because of limited capabilities in perception and interaction with complex trafic environments. Cooperative Driving Automation (CDA) has been considered a pivotal solution to next-generation automated driv-ing and smart transportation. Though CDA has attracted much attention from both academia and industry, exploration of its potential is still in its infancy. In industry, companies tend to build their in-house data collection pipeline and research tools to tailor their needs and protect intellectual properties. Reinventing the wheels, however, wastes resources and limits the generalizability of the developed approaches since no standardized benchmarks exist. On the other hand, in academia, due to the absence of real-world trafic data and computation resources, researchers often investigate CDA topics in simplified and mostly simulated environments, restricting the possibility of scaling the research outputs to real-world scenarios. Therefore, there is an urgent need to establish an open-source ecosystem (OSE) to address the demands of different communities for CDA research, particularly in the early exploratory research stages, and provide the bridge to ensure an integrated development and testing pipeline that diverse communities can share. In this paper, we introduce the OpenCDA research ecosystem, a unified OSE integrated with a model zoo, a suite of driving simulators at various resolutions, large-scale real-world and simulated datasets, complete devel-opment toolkits for benchmark training/testing, and a scenario database/generator. We also demonstrate the effectiveness of OpenCDA OSE through example use cases, including cooperative 3D LiDAR detection, cooperative merge, cooperative camera-based map prediction, and adversarial scenario generation.

Indirect Shared Control Strategy for Human-Machine Cooperative Driving on Hazardous Curvy Roads

Effective and friendly human-machine cooperative driving is considered as a feasible solution to filling the gap between assisted driving and highly automated driving. This paper proposes an indirect shared control strategy, in both longitudinal and lateral directions, for safe cooperative driving on curvy roads, which often pose hazards in real-world driving. At the tactical decision level of the proposed strategy, driving authorities are dynamically allocated between human and machine based on the risk assessed with a data-driven gaussian processes regression (GPR) model, which is trained to generate dynamic safety envelopes of longitudinal and lateral control commands, namely vehicle speed and front wheel steering angle, from observed road conditions and vehicle states. At the maneuver execution level, a multi-objective hierarchical MPC controller is built to combine human-machine control commands smoothly for collision-free driving while simultaneously maintaining vehicle stability and mitigating human-machine conflicts. Both matching the driver's commands and tracking the reference trajectories are formulated into objectives of the optimization-based controller. The weight factor of each objective is adjusted according to the authorities allocated at the decision level. The results of experiments conducted on a 3 degree of freedom motion-base driving simulator show that the proposed strategy accomplishes effective and friendly cooperative driving on curvy roads. It's also verified that the proposed strategy is adaptable to unseen scenarios, mainly owing to the superiority of the GPR-based risk assessment model, driven by class-balanced training data, in modeling the nonlinearity and uncertainty of driving risks.

Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform

Endowed with automation and connectivity, connected and automated vehicles (CAVs) will be a revolutionary promoter for cooperative driving automation (CDA). Nevertheless, CAVs need high-fidelity perception information on their surroundings, which is available but costly to collect from various onboard sensors, as well as vehicle-to-everything communications. Therefore, authentic perception (AP) information based on high-fidelity sensors via a cost-effective platform is crucial for enabling CDA-related research, e.g., cooperative decision making or control. Most state-of-the-art traffic simulation studies for CAVs rely on the situation-awareness information by directly calling on intrinsic attributes of the objects, which impedes the reliability and fidelity of the assessment of CDA algorithms. In this study, a cyber mobility mirror (CMM) co-simulation platform is designed for enabling CDA by providing AP information. The CMM co-simulation platform can emulate the real world with a high-fidelity sensor perception system and the cyberworld with a real-time rebuilding system acting as a “mirror” of the real-world environment. Concretely, the real-world simulator is mainly in charge of simulating the traffic environment, the sensors, and the AP process. The mirror-world simulator is responsible for representing objects and providing their information as intrinsic attributes of the simulator to support the development and evaluation of CDA algorithms. To illustrate the functionality of the proposed co-simulation platform, a roadside lidar-based vehicle perception system for enabling CDA is prototyped as a study case. Specific traffic environments and CDA tasks are designed for experiments whose results are demonstrated and analyzed to show the performance of the platform.

Fault-Tolerant Cooperative Driving at Signal-Free Intersections

Cooperative driving shows great potential to improve traffic safety and efficiency and has been well discussed in recent years. However, most existing studies focus on ideal traffic environments and ignore potential vehicle failures in traffic systems, which pose significant threats to traffic safety. Therefore, the fault-tolerant capacity of the existing cooperative driving strategies is questionable. To fill this research gap, this paper proposes a fault-tolerant cooperative driving strategy for signal-free intersections by modeling potential vehicle failure types, aiming to keep a good balance between traffic safety and efficiency. Notably, a rule-based fault-tolerant model is constructed to mitigate the threat of potential vehicle failures to traffic safety and efficiency, and to effectively recover the cooperative driving system after vehicle failures occur. Theoretical analysis and simulation results jointly demonstrate the promising performance of the proposed model in achieving fault tolerance and improving traffic efficiency.

Leveraging Autonomous Vehicles to Tally Cooperative Driving Behavior

The number of Autonomous Vehicles (AVs) coexisting with conventional human-driven vehicles is expected to increase significantly in the coming years. This coexistence will last decades before full AV adoption is achieved worldwide. However, the cautious nature of AVs and the aggressive behavior of some human drivers could create unprecedentedly challenging scenarios for AVs, such as being stuck on merge lanes and blocked by human-driven vehicles. On the other hand, the cooperative behavior of other human drivers could assist AVs in avoiding deadlock situations. In this paper, we propose to leverage AVs to tally the cooperative driving behavior of human-driven vehicles. To this end, we model cooperative driving behavior in a “highway merge” scenario, which tends to be challenging for AVs. We vary the percentage of cooperative human-driven vehicles and estimate the percentage of AVs required to tally cooperative acts. Results show that when fifty percent of the human drivers cooperate, cooperation leads to statistically significant reductions of up to 68%, 46%, 38%, and 5% in stop delay, number of stops, vehicle delay, and travel time, respectively. Finally, we demonstrate that a 30% penetration of AVs is sufficient to tally up to 78% of cooperative behavior in highway scenarios. To promote cooperation across the population, our future work revolves around the construction of vehicular profiles based on their cooperative behavior. These profiles will be regularly updated and disseminated among AVs to aid their cooperative decisions toward human-driven vehicles in the upcoming interactions.

Research on Control Authority Transfer Algorithm for the Intelligent Vehicle with Human-machine Cooperative Driving

Research on Control Authority Transfer Algorithm for the Intelligent Vehicle with Human-machine Cooperative Driving

通讯网络影响下多车协同驾驶控制策略研究

通讯网络影响下多车协同驾驶控制策略研究

Signalling Design in Sensor-Assisted mmWave Communications for Cooperative Driving

Signalling Design in Sensor-Assisted mmWave Communications for Cooperative Driving

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.