Cooperative Driving Research Articles

A Cooperative Driving Strategy Based on Velocity Prediction for Connected Vehicles With Robust Path-Following Control

The autonomous vehicles need to cooperate with the nearby vehicles to ensure driving safety, however, it is challenging to plan and follow the desired trajectory considering the nearby vehicles. This article proposes a cooperative driving strategy for the connected vehicles by integrating vehicle velocity prediction, motion planning, and robust fuzzy path-following control. The system uncertainties are considered to enhance the cooperation between the autonomous vehicle and the nearby vehicle. With the driving information obtained from the connected vehicles technique, the recurrent neural network is used to predict the nearby vehicle velocity. A motion planner is developed to provide the reference trajectory considering the velocity prediction errors. Then, a robust fuzzy path-following controller is designed to track the planned trajectory. The CarSim simulations are conducted to validate the proposed cooperative driving strategy. The simulation results show that the autonomous vehicle can avoid collisions with the nearby vehicle by applying the proposed driving strategy in the overtaking and the lane-changing scenarios.

Consensus Phenomenon and Characteristics of Vehicle Cooperative Driving

AbstractThe consensus degree among vehicles is an important index to measure the cooperative driving system. First, this paper establishes a measurement method of the instantaneous consensus of an ...

Detection of Driving Capability Degradation for Human-Machine Cooperative Driving.

Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.

A Secure Adaptive Control for Cooperative Driving of Autonomous Connected Vehicles in the Presence of Heterogeneous Communication Delays and Cyberattacks.

The development of autonomous connected vehicles, moving as a platoon formation, is a hot topic in the intelligent transportation system (ITS) research field. It is on the road and deployment requires the design of distributed control strategies, leveraging secure vehicular ad-hoc networks (VANETs). Indeed, wireless communication networks can be affected by various security vulnerabilities and cyberattacks leading to dangerous implications for cooperative driving safety. Control design can play an important role in providing both resilience and robustness to vehicular networks. To this aim, in this article, we tackle and solve the problem of cyber-secure tracking for a platoon that moves as a cohesive formation along a single lane undergoing different kinds of cyber threats, that is, application layer and network layer attacks, as well as network induced phenomena. The proposed cooperative approach leverages an adaptive synchronization-based control algorithm that embeds a distributed mitigation mechanism of malicious information. The closed-loop stability is analytically demonstrated by using the Lyapunov-Krasovskii theory, while its effectiveness in coping with the most relevant type of cyber threats is disclosed by using PLEXE, a high fidelity simulator which provides a realistic simulation of cooperative driving systems.

Driving Capability-Based Transition Strategy for Cooperative Driving: From Manual to Automatic

In open traffic environments, humans still have to remain in the control loop of vehicle due to the insufficient of the existing technologies and their high costs. For the realization of cooperation between the human and the automatic driving system, the determination of the time when automatic driving is necessary is very important. To avoid unnecessary intervention when the driver has the control authority of vehicle, a new driving capability-based transition strategy was proposed, which comprehensively considers the driver’s correction ability and the driving risk. The transition time from the human driver to the automatic driving system is determined by an unreliable domain (UD), whose boundary is modeled according to the driving data recorded by a driving simulator and statistically described by a log-normal distribution. Furthermore, an adaptive algorithm is designed to update the parameters of UD boundary online to make this strategy suitable for different drivers. This UD-based transition strategy is validated by several tests on the driving simulator. The bench test results show that the individual driving characteristic can be identified by the adaptive algorithm in time, the transition time determined by UD is more accurate, and sufficient time is reserved for the correction carried out by the automatic driving system.

Assessment of Drivers’ Comprehensive Driving Capability Under Man-Computer Cooperative Driving Conditions

The effective assessment of drivers' driving capability under the condition of an advanced driver assistance system is of great significance to the precise switching of driving rights between human and machine and the promotion of the development of man-computer collaboration. In this study, real-time collected warning data from bus driver state monitoring system (DSMS) and advanced driver assistance system (ADAS) were utilized to determine the drivers' comprehensive driving capability indicators. The information utility and interaction of the indicators were considered, and an integrated weight method based on standard deviations was proposed. This method was used to combine the entropy weight method and improved analytic network process (ANP), to evaluate the drivers' comprehensive driving capability under man-computer cooperative driving conditions in real time. The results show that the entropy weight method and improved ANP algorithm have good consistency and are significantly correlated and that the integrated weight method is effective and dependable. The top four indicators in the integrated weighting results were eye closure (0.241), yawn (0.210), rapid deceleration (0.186), and lane departure (0.159). Drivers' comprehensive driving capability scores were concentrated in the score range of 1 to 6, with the lowest scores in zones A and B for stages 2, 11 and 21. Therefore, it is necessary to further explore the relationship between driver behavior, vehicle status and road traffic environment within the score range of 1 to 6 so that the man-computer interaction can be optimized and the driver's comprehensive driving capability can be improved.

Analysis of Cooperative Driving Replacement Behavior between Humans assuming Autonomous Driving

Analysis of Cooperative Driving Replacement Behavior between Humans assuming Autonomous Driving

A Cooperative Driving Strategy for Merging at On-Ramps Based on Dynamic Programming

Cooperative driving emerges as a promising way to improve efficiency and safety for Connected and Automated Vehicles (CAVs). Its key idea is to design a strategy to schedule the movements of neighboring vehicles. The typical cooperative driving strategies can be categorized into two categories. The first category is optimal strategy, which aims to find the globally optimal passing order of vehicles, but the computational cost of this strategy grows significantly with the increasing number of vehicles. The second category is sub-optimal strategy, which uses heuristic rules or other methods to export an acceptable local optimal solution within a limited computation time. However, there usually lacks a rigorous theoretical guarantee of the performances, and further validation is always required for practical applications. To overcome all these limitations, a computationally efficient strategy is proposed to obtain the globally optimal passing order based on dynamic programming (DP). Specifically, the problem of merging at on-ramps is resolved by a DP method, which uses the domain knowledge to reduce the complexity by well defining the state space, state transition, and criterion function. With the DP method, it is proved that the globally optimal passing order can be obtained with the quadratic polynomial computational complexity of $O(N^2)$ , where $N$ denotes the number of vehicles. Simulation results demonstrate the performances of the proposed strategy regarding optimality and efficiency.

Movement Prediction Hypotheses for Pedestrians and Trajectory Planning for Cooperative Driving Systems

Movement Prediction Hypotheses for Pedestrians and Trajectory Planning for Cooperative Driving Systems

Cooperative Driving at Unsignalized Intersections Using Tree Search

In this paper, we propose a new cooperative driving strategy for connected and automated vehicles (CAVs) at unsignalized intersections. Based on the tree representation of the solution space for the passing order, we combine Monte Carlo tree search (MCTS) and some heuristic rules to find a nearly global-optimal passing order (leaf node) within a very short planning time. Testing results show that this new strategy can keep a good tradeoff between performance and computation flexibility.

A Grouping-Based Cooperative Driving Strategy for CAVs Merging Problems

In general, there are two kinds of cooperative driving strategies, planning based strategy and ad hoc negotiation based strategy, for connected and automated vehicles (CAVs) merging problems. The planning based strategy aims to find the global optimal passing order, but it is time-consuming when the number of considered vehicles is large. In contrast, the ad hoc negotiation based strategy runs fast, but it always finds a local optimal solution. In this paper, we propose a grouping based cooperative driving strategy to make a good tradeoff between time consumption and coordination performance. The key idea is to fix the passing orders for some vehicles whose inter-vehicle headways are small enough (e.g., smaller than the pre-selected grouping threshold). From the viewpoint of optimization, this method reduces the size of the solution space. A brief analysis shows that the sub-optimal passing order found by the grouping based strategy has a high probability to be close to the global optimal passing order, if the grouping threshold is appropriately chosen. A series of simulation experiments are carried out to validate that the proposed strategy can yield a satisfied coordination performance with less time consumption and is promising to be used in practice.

An Overview of Cooperative Driving in the European Union: Policies and Practices

Cooperative-Intelligent Transportation Systems (C-ITSs) aim to connect vehicles, both with one another and with road infrastructures, so as to increase traffic safety and efficiency. This paper focuses on the European framework for supporting the development of Cooperative, Connected, and Automated Mobility, and aims to shed light on the current state of testing and deployment activities in the field at the start of 2019. This may be considered particularly timely given that the year 2019 was identified as the starting date for the deployment of mature services, and the Community legislation is currently paying great attention to the matter. In order to present a concise (but comprehensive) picture, we consulted and analysed the most diverse sources comprising more than 2000 pages.

Research on a Task Planning Method for Multi-Ship Cooperative Driving

A new method for a cooperative multi-task allocation problem (CMTAP) is proposed in this paper, taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship cooperative driving (MCD) system. On the basis of the general CMTAP model, an MCD task assignment model is established. Furthermore, a genetic ant colony hybrid algorithm (GACHA) is proposed for this model using constraints, including timing constraints, multi-ship collaboration constraints and ship capacity constraints. This algorithm uses a genetic algorithm (GA) based on a task sequence, while the crossover and mutation operators are based on similar tasks. In order to reduce the dependence of the GA on the initial population, an ant colony algorithm (ACA) is used to produce the initial population. In order to meet the environmental constraints of ship navigation, the results of the task allocation and path planning are combined to generate an MCD task planning scheme. The results of a simulated experiment using simulated data show that the proposed method can make the assignment more optimized on the basis of satisfying the task assignment constraints and the ship navigation environment constraints. Moreover, the experimental results using real data also indicate that the proposed method can find the optimal solution rapidly, and thus improve the task allocation efficiency.

Cooperative Driving and the Tactile Internet

The trend toward autonomous driving and the recent advances in vehicular networking led to a number of very successful proposals in cooperative driving. Maneuvers can be coordinated among participating vehicles and controlled by means of wireless communications. One of the most challenging scenarios or applications in this context is cooperative adaptive cruise control (CACC) or platooning. When it comes to realizing safety gaps between the cars of less than 5 m, very strong requirements on the communication system need to be satisfied. The underlying distributed control system needs regular updates of sensor information from the other cars in the order of about 10 Hz. This leads to message rates in the order of up to 10 kHz for large networks, which, given the possibly unreliable wireless communication and the critical network congestion, is beyond the capabilities of current vehicular networking concepts. In this paper, we summarize the concepts of networked control systems and revisit the capabilities of current vehicular networking approaches. We then present opportunities of Tactile Internet concepts that integrate interdisciplinary approaches from control theory, mechanical engineering, and communication protocol design. This way, it becomes possible to solve the high reliability and latency issues in this context.

A Study on Driver-vehicle Interface for Cooperative Driving

A Study on Driver-vehicle Interface for Cooperative Driving

Movement Prediction Hypotheses for Pedestrians and Trajectory Planning for Cooperative Driving Systems

Movement Prediction Hypotheses for Pedestrians and Trajectory Planning for Cooperative Driving Systems

MicroIV: A Cooperative Driving Hardware Simulation Platform for Cooperative-ITS

Cooperative-ITS (C-ITS) is a rising innovation that involves the integration of existing intelligent vehicle technology and communication technology, which allows information exchange among components and autonomous cooperative driving among vehicles. Although some hardware platforms are developed for simulation of C-ITS applications, there are still some problems such as lack of global positioning, limited simulation scenarios, and high cost. This paper presents Micro Intelligent Vehicle (MicroIV)-based cooperative driving hardware simulation platform for simulation of C-ITS applications, especially the cooperative maneuvers. Different from existing platforms, cameras installed around the test field are used to capture the motion and global positioning information of micro vehicles, similar to GPS methods. Such system architecture reduces the vehicle's hardware configuration and significantly cuts the cost of the whole system. Then, it is easy to expand the scale of the experiments. Therefore, the proposed platform is flexible for different cooperation scenarios. Point-to-multipoint communication structure with the fixed master node is used to implement the communication between vehicles. Interacting Multiple Model method is leveraged to improve positioning accuracy. Experiments are conducted to prove that the proposed MicroIV platform can meet the positioning accuracy requirement of C-ITS application. Then, applications for both V2V and V2I cooperation are presented: platoon with 20 vehicles, intersection cooperation with 8 vehicles, merging, and automatic parking. These experiments prove the feasibility and reliability of the proposed platform for cooperative driving hardware simulation.

Robust Stabilization and H∞ Control of Cooperative Driving System with Time Delay in Variable Speed‐Limited Area from Cyber‐Physical Perspective

AbstractIn order to suppress traffic congestion and reduce traffic accidents, a cooperative driving systems with time‐varying delay and nonlinearity under uncertain external disturbances in a variable speed‐limited area is proposed from a cyber‐physical perspective. Robust stabilization of the cooperative driving system is investigated by using Lyapunov‐Krasovskii functional stability theory. Robust H∞ control is designed to guarantee that the proposed system is robustly stable. Meanwhile, sufficient conditions for the state feedback controller are proposed to attenuate the external disturbances on the basis of linear matrix inequality. Finally, some useful results are obtained from the comparisons between without control scheme, existing ACC (adaptive cruise control) scheme, and the proposed control scheme, which could suppress traffic congestion and reduce traffic accidents.

Team Halmstad Approach to Cooperative Driving in the Grand Cooperative Driving Challenge 2016

This paper is an experience report of team Halmstad from the participation in a competition organised by the i-GAME project, the Grand Cooperative Driving Challenge 2016. The competition was held in Helmond, The Netherlands, during the last weekend of May 2016. We give an overview of our car’s control and communication system that was developed for the competition following the requirements and specifications of the i-GAME project. In particular, we describe our implementation of cooperative adaptive cruise control, our solution to the communication and logging requirements, as well as the high level decision making support. For the actual competition we did not manage to completely reach all of the goals set out by the organizers as well as ourselves. However, this did not prevent us from outperforming the competition. Moreover, the competition allowed us to collect data for further evaluation of our solutions to cooperative driving. Thus, we discuss what we believe were the strong points of our system, and discuss post-competition evaluation of the developments that were not fully integrated into our system during competition time.

Analysis of Cooperative Driving Strategies for Nonsignalized Intersections

In this paper, we study the difference between two major strategies of cooperative driving at nonsignalized intersections: namely the “ad hoc negotiation-based” strategy and the “planning-based” strategy. The fundamental divide of these two strategies lies in how to determine the passing order of vehicles at intersections. The “ad hoc negotiation-based” strategy makes vehicles roughly follow first-come-first-served order but allows some adjustments. This leads to a local optimal solution in many situations. The “planning-based” strategy aims to find a global optimal passing order of vehicles. However, constrained by the planning complexity and time requirement, we often stop at a local optimal solution, too. We carry out a series of simulations to compare the solutions found by two strategies, under different traffic scenarios. Results indicate the performance of a strategy mainly depends on the passing order of vehicles that it finds. Although there exist several trajectory planning algorithms associating with the solving process of passing orders, their differences are negligible. Moreover, if the traffic demand is very low, the performance difference between two strategies is small. When the traffic demand becomes high, the “planning-based” strategy yields significantly better performance since it finds better passing orders. These findings are important to cooperative driving study.