Cooperative Adaptive Research Articles

Cooperative ecological cruising using hierarchical control strategy with optimal sustainable performance for connected automated vehicles on varying road conditions

Facing the increasingly severe energy and environmental problems, platooning of multiple vehicles has great potential for sustainability of intelligent transportation systems. This study develops a novel hierarchical ECACC strategy with energy-saving achievements by conducting the ecological velocity trajectory planning of the leading vehicle based on dynamic programming and car-following driving of the rest of the vehicles in the platoon based on combined feedforward-feedback control. The main purpose is to reduce the energy consumption of multiple vehicles in intelligent transportation systems with consideration of trip time and car-following performance. The optimal velocity planning uses an innovative integrated longitudinal and lateral vehicle dynamics approach where both the lateral stability and energy efficiency in continuous curved roads are guaranteed at the same time. The results show that the proposed ECACC in energy-optimal mode can reduce energy consumption by up to 38.1% compared to conventional energy-optimal fixed speed cruising. Moreover, the connected automated vehicle platoon presents better car-following performance with string stability as opposed to the Cooperative Adaptive Cruise Control based on feedback control and model predictive control. The results indicate the great potential of the proposed ECACC strategy to further improve the sustainability of intelligent transportation systems.

Adequacy of Manitoba concrete bridge rail during truck platoon impacts and associated occupant risks

Platooning is an extension of Cooperative Adaptive Cruise Control (CACC) and forward collision avoidance technology; it allows lateral and longitudinal control of vehicles while they move in tight formations. Platooning is ideal for trucks as they usually travel long distances in highways as a group. It is necessary to understand if the existing roadside safety devices are adequate to resist the consecutive impacts at high speed that can occur due to errant truck platoons. It is also important to analyze the associated occupant risks during such impact events. A detailed analysis of the capacity and adequacy of Manitoba Concrete Bridge Rail using computer simulations, under Manual for Assessing Safety Hardware (MASH) Test Level 5 (TL5) criteria, is presented in this study. The study briefly discusses the methodology to run multiple impact computer simulations in LS-DYNA. The impact of the leading truck is validated using the known data from a full-scale crash test; then, the impact events involving the following trucks are simulated to understand the additional capacity of the roadside barrier. To assess the occupant risk, the flail space model (FSM) concept is utilised as per MASH criteria. Simulation results imply that catastrophic barrier failure and major injuries to the occupants are unlikely to occur during the truck platoon impact.

Mobility and Energy Consumption Impacts of Cooperative Adaptive Cruise Control Vehicle Strings on Freeway Corridors

Cooperative adaptive cruise control (CACC) vehicle string operations have the potential to improve significantly the mobility and energy consumption performance of congested freeway corridors. This study examines the impact of CACC string operations on vehicle speed and fuel economy on the 13-mi SR-99 corridor, near Sacramento, CA. It extends the existing body of knowledge by performing a multi-scenario simulation analysis of the freeway corridor. A simulation study evaluated the performance of the corridor under various CACC market penetration scenarios and traffic demand inputs. The CACC string operation was also analyzed when vehicle awareness device (VAD) and CACC managed lane (ML) strategies were implemented. The case study revealed that the average vehicle speed increased by 70% when the CACC market penetration increased from 0% to 100%. The highest average fuel economy, expressed in miles per gallon (mpg), was achieved under the 50% CACC scenario where mpg was 27. This was 10% higher than the baseline scenario. However, when the CACC market penetration was 50% or higher, the vehicle fuel efficiency only had minor increases. When CACC market penetration reached 100%, the corridor allowed 30% more traffic to enter the network without experiencing reduced average speed. Results also indicate that the VAD strategy increased the speed by 8% when the CACC market penetration was 20% or 40%, while there was a minor decrease in mpg. The ML strategy decreased the corridor performance when implemented alone.

A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions

Motivated by connected and automated vehicle (CAV) technologies, this paper proposes a data-driven optimization-based Model Predictive Control (MPC) modeling framework for the Cooperative Adaptive Cruise Control (CACC) of a string of CAVs under uncertain traffic conditions. The proposed data-driven optimization-based MPC modeling framework aims to improve the stability, robustness, and safety of longitudinal cooperative automated driving involving a string of CAVs under uncertain traffic conditions using Vehicle-to-Vehicle (V2V) data. Based on an online learning-based driving dynamics prediction model, we predict the uncertain driving states of the vehicles preceding the controlled CAVs. With the predicted driving states of the preceding vehicles, we solve a constrained Finite-Horizon Optimal Control problem to predict the uncertain driving states of the controlled CAVs. To obtain the optimal acceleration or deceleration commands for the CAVs under uncertainties, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e. a special case of data-driven optimization models under moment bounds) with a Distributionally Robust Chance Constraint (DRCC). The predicted uncertain driving states of the immediately preceding vehicles and the controlled CAVs will be utilized in the safety constraint and the reference driving states of the DRSO-DRCC model. To solve the minimax program of the DRSO-DRCC model, we reformulate the relaxed dual problem as a Semidefinite Program (SDP) of the original DRSO-DRCC model based on the strong duality theory and the Semidefinite Relaxation technique. In addition, we propose two methods for solving the relaxed SDP problem. We use Next Generation Simulation (NGSIM) data to demonstrate the proposed model in numerical experiments. The experimental results and analyses demonstrate that the proposed model can obtain string-stable, robust, and safe longitudinal cooperative automated driving control of CAVs by proper settings, including the driving-dynamics prediction model, prediction horizon lengths, and time headways. Computational analyses are conducted to validate the efficiency of the proposed methods for solving the DRSO-DRCC model for real-time automated driving applications within proper settings.

Control Design, Stability Analysis, and Traffic Flow Implications for Cooperative Adaptive Cruise Control Systems with Compensation of Communication Delay

Communication delay is detrimental to the performance of cooperative adaptive cruise control (CACC) systems. In this paper, we incorporate communication delay explicitly into control design and propose a delay-compensating CACC. In this new CACC system, the semi-constant time gap (Semi-CTG) policy, which is modified on the basis of the widely-used CTG policy, is employed by a linear feedback control law to regulate the spacing error. The semi-CTG policy uses historical information of the predecessor instead of its current information. By doing so, communication delay is fully compensated, which leads to better stability performance. Three stability properties—local stability, string stability, and traffic flow stability—are analyzed. The local stability and string stability of the proposed CACC system are guaranteed with the desired time gap as small as the communication delay. Both theoretical analysis and simulation results show that the delay-compensating CACC has better string stability and traffic flow stability than the widely-used CACC system. Furthermore, the proposed CACC system also shows the potential for improving traffic throughput and fuel efficiency. Robustness of the proposed system against uncertainties of sensor delay and vehicle dynamics is also verified with simulation.

Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

Connected and automated vehicle technologies have the potential to significantly improve transportation system performance. In particular, advanced driver-assistance systems, such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), may lead to substantial improvements in performance by decreasing driver inputs and taking over control of the vehicle. However, the impacts of these technologies on the vehicle- and system-level energy consumption, emissions, and safety have not been quantified in field tests. The goal of this paper is to study the impacts of automated and cooperative systems in mixed traffic containing conventional, ACC, and CACC vehicles. To reach this goal, experimental data based on real-world conditions are collected (in tests conducted by the Federal Highway Administration and the U.S. Department of Transportation) with presence of ACC, CACC, and conventional vehicles in a vehicle platoon scenario and a cooperative merging scenario. Specifically, a platoon of five vehicles with different vehicle type combinations is analyzed to generate new knowledge about potential safety, energy efficiency, and emission improvement from vehicle automation and cooperation. Results show that adopting the CACC system in a five-vehicle platoon substantially reduces the driving volatility and reduces the risk of rear-end collision which consequently improves safety. Furthermore, it decreases fuel consumption and emissions compared with the ACC system and manually-driven vehicles. Results of the merging scenario show that while the cooperative merging system slightly reduces the driving volatility, the fuel consumption and emissions can increase because of sharper accelerations of CACC vehicles compared with manually-driven vehicles.

Adaptive cooperation for free space optical communications

In this paper, we propose a new Adaptive Cooperation (AC) protocol for Free Space Optical (FSO) communications. Two versions of the protocol are presented. The first protocol uses the Instantaneous Signal to Noise Ratio (ISNR) to activate the relay only when the corresponding instantaneous throughput is better than that of the direct link. The second protocol uses the Average SNR (ASNR) to activate the relay when its average throughput is larger than that of the direct link. The proposed AC protocol has been extended to include Adaptive Modulation and Coding (AMC). Our results are valid for FSO communications in the presence of Gamma Gamma atmospheric turbulence.

Cooperative Adaptive Dynamic Surface Control for a Class of High-Order Stochastic Nonlinear Multiagent Systems.

This article investigates the consensus tracking problem for high-order stochastic pure-feedback nonlinear multiagent systems (MASs) with dead zones. It should be pointed out that each follower's virtual and actual control items are the power-exponential functions with positive odd numbers instead of linear items. Because of the structural characteristics of the followers' dynamics, a technique called adding a power integrator is used, which effectively overcomes the difficulties of states and dead zone with power-exponential functions. Furthermore, radial basis function neural networks are employed to estimate unknown nonlinear functions and solve the problem of algebraic loop caused by the pure-feedback structure of MASs. Meanwhile, the problems of "explosion of complexity" caused by repeated differentiations of the virtual controller are solved by using the tracking differentiators. Based on the Lyapunov stability theorem, it is proved that all signals of the closed-loop systems are semiglobally uniformly ultimately bounded in probability, and the tracking errors can converge to a small neighborhood of the origin. Finally, simulation results are presented to verify the effectiveness of the proposed approach.

Cooperative Adaptive Cruise Control With Robustness Against Communication Delay: An Approach in the Space Domain

In this research, an optimal control-based Cooperative Adaptive Cruise Control (CACC) system is proposed. The proposed system is able to enforce a target time gap between platoon members and is formulated in the space domain instead of the time domain which is adopted by most optimal control-based CACC systems in the past. By having this change, its robustness against communication failure is greatly improved and thus minimum safety headway buffer is reduced which leads to better mobility. In addition, third-order vehicle dynamics are modeled into the proposed control in order to improve control precision when implemented in the field. Local stability and string stability are theoretically proven. The proposed system is evaluated by simulation. Results reveal that the proposed CACC system outperforms the state-of-the-art <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> synthesis-based controller and linear feedback-based controller. The benefit of fuel consumption reduction ranges from 0.35% to 16.11%, while the benefit of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission ranges from 0.48% to 12.40%. Furthermore, the proposed CACC improves local stability from 11.03% to 25.90%, and string stability by up to 23.82%. The computation speed of the proposed method is 1.26 ms (with prediction horizon as 1.5 s and resolution as 0.1 s) on a regular laptop which indicates the proposed system's potential to be applied in real-time.

Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains.

In this article, a cooperative adaptive iterative learning fault-tolerant control (CAILFTC) algorithm with the radial basis function neural network (RBFNN) is proposed for multiple subway trains subject to the time-iteration-dependent actuator faults by using the multiple-point-mass dynamics model. First, an RBFNN is utilized to cope with the unknown nonlinearity of the subway train system. Next, a composite energy function (CEF) technique is applied to obtain the convergence property of the presented CAILFTC, which can guarantee that all train speed tracking errors are asymptotic convergence along the iteration axis; meanwhile, the headway distances of neighboring subway trains are kept in a safety range. Finally, the effectiveness of theoretical studies is verified through a subway train simulation.

An adaptive cooperation with reinforcement learning for robot soccer games

A strategy system with self-improvement and self-learning abilities for robot soccer system has been developed in this study. This work focuses on the cooperation strategy for the task assignment and develops an adaptive cooperation method for this system. This method was inspired by reinforcement learning (RL) and game theory. The developed system includes two subsystems: the task assignment system and the RL system. The task assignment system assigns one of the four roles, Attacker, Helper, Defender, and Goalkeeper, to each separate robot with the same physical and mechanical conditions to achieve cooperation. The assigned role to robots considers the situation in the game field. Each role has its own behaviors and tasks. The RL helps the Helper and Defender to improve the ability of their policy selection on the real-time confrontation. The RL system can not only learn to figure up how Helper helps its teammates to form an attack or a defense type but also learn to stand a proper defensive strategy. Some experiments on FIRE simulator and standard platform have been demonstrated that the proposed method performs better than the competitors.

Anomaly Detection for Cooperative Adaptive Cruise Control in Autonomous Vehicles Using Statistical Learning and Kinematic Model

This paper focuses on Cooperative Adaptive Cruise Control (CACC) in autonomous vehicles. In CACC, vehicles regulate their speed according to a preceding “leader” vehicle in the lane, forming a platoon. In a benign environment, CACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, CACC is vulnerable to various security threats. In this paper, we consider one of the critical threats, where the platoon leader is compromised, and forges acceleration information sent to platoon members. Such attack would lead to traffic instability and potential collisions. First, we propose information sharing in CACC model to allow vehicles and fixed infrastructure to sense and share information about platoon leaders, hence improves the reliability and supports the detection of anomalous behavior. Then, we propose a real-time anomaly detection mechanism that combines statistical learning with the physics laws of kinematics. Specifically, we propose Generalized Extreme Studentized Deviate with Sliding Chunks (GESD-SC) approach, which is applied at each vehicle in the platoon to detect anomalies in real-time based on the vehicle's own speeding decisions. Kinematic model is also utilized to detect unexpected deviations using the leader's information, communicated directly and observed by the leader's neighboring vehicle(s) and/or supporting infrastructure. Combining kinematic model with GESD-SC has shown to be effective in detecting falsification attacks in CACC. Furthermore, we analyze the time performance, and show that the proposed technique outperforms existing method in detection accuracy and processing time.

Multi-Vehicle Trajectory Design During Cooperative Adaptive Cruise Control Platoon Formation

Cooperative adaptive cruise control (CACC) organizes connected and automated vehicles (CAVs) in platoons to improve traffic flow and reduce fuel consumption. Platoon formation involves a very complex process, however, because lateral and longitudinal misbehavior of CAVs results in greater fuel consumption and risk of collision. This study aims to design optimal vehicle trajectories of CAVs during CACC platoon formation. First, a basic scenario and a destination-based protocol are described to determine vehicle sequence in the platoon. A space-time lattice based model is then formulated to construct vehicle trajectories considering boundary conditions of kinematic limits, vehicle-following safety, and lane-changing rules. The objective is to optimize the vehicle sequence and fuel consumption simultaneously. A two-phase algorithm is proposed to solve this model, where the first phase is a heuristic algorithm that determines vehicle sequence and in the second phase dynamic programming is adapted to optimize fuel consumption based on the determined sequence. To evaluate the effectiveness of the proposed model in designing CAV trajectories, extensive experimental tests have been conducted in this study. Results show that the proposed model and algorithm can effectively optimize CAV sequence in the platoon based on their destinations. After optimization, CAV fuel consumption was reduced by 42%, 46%, and 43%, respectively, in three different tested scenarios.

Influence of CAV clustering strategies on mixed traffic flow characteristics: An analysis of vehicle trajectory data

Being one of the most promising applications enabled by connected and automated vehicles (CAV) technology, Cooperative Adaptive Cruise Control (CACC) is expected to be deployed in the near term on public roads. Thus far, the majority of the CACC studies have been focusing on the overall network performance with limited insights on the potential impacts of CAVs on human-driven vehicles (HVs). This paper aims to quantify such impacts by studying the high-resolution vehicle trajectory data that are obtained from microscopic simulation. Two platoon clustering strategies for CACC- an ad hoc coordination strategy and a local coordination strategy-are implemented. Results show that the local coordination outperforms the ad hoc coordination across all tested market penetration rates (MPRs) in terms of network throughput and productivity. According to the two-sample Kolmogorov-Smirnov test, however, the distributions of the hard braking events (as a potential safety impact) for HVs change significantly under local coordination strategy. For both of the clustering strategy, CAVs increase the average lane change frequency for HVs. The break-even point for average lane change frequency between the two strategies is observed at 30% MPR, which decreases from 5.42 to 5.38 per vehicle. The average lane change frequency following a monotonically increasing pattern in response to MPR, and it reaches the highest 5.48 per vehicle at 40% MPR. Lastly, the interaction state of the car-following model for HVs is analyzed. It is revealed that the composition of the interaction state could be influenced by CAVs as well. One of the apparent trends is that the time spent on approaching state declines with the increasing presence of CAVs.

Smooth-Switching Control-Based Cooperative Adaptive Cruise Control by Considering Dynamic Information Flow Topology

Vehicle-to-vehicle communications can be unreliable because of interference and information congestion, which leads to the dynamic information flow topology (IFT) in a platoon of connected and autonomous vehicles. Some existing studies adaptively switch the controller of cooperative adaptive cruise control (CACC) to optimize string stability when IFT varies. However, the difference of transient response between controllers can induce uncomfortable jerks at switching instances, significantly affecting riding comfort and jeopardizing vehicle powertrain. To improve riding comfort while maintaining string stability, the authors introduce a smooth-switching control-based CACC scheme with IFT optimization (CACC-SOIFT) by implementing a bi-layer optimization model and a Kalman predictor. The first optimization layer balances the probability of communication failure and control performance optimally, generating a robust IFT to reduce controller switching. The second optimization layer adjusts the controller parameters to minimize tracking error and the undesired jerk. Further, a Kalman predictor is applied to predict vehicle acceleration if communication failures occur. It is also used to estimate the states of preceding vehicles to suppress the measurement noise and the acceleration disturbance. The effectiveness of the proposed CACC-SOIFT is validated through numerical experiments based on NGSIM field data. Results indicate that the CACC-SOIFT framework can guarantee string stability and riding comfort in the environment of dynamic IFT.

Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs

Electrification, automation, and connectivity in the automotive and transport industries are gathering momentum, but there are escalating concerns over their need for co-optimization to improve energy efficiency, traffic safety, and ride comfort. Previous approaches to these multiobjective co-optimization problems often overlook tradeoffs and scale differences between the objectives, resulting in misleading optimizations. To overcome these limitations, this article proposes a Pareto-based framework that demonstrably optimizes the system parameters of the cooperative adaptive cruise control (CACC) and the energy management strategy (EMS) for plug-in hybrid electric vehicles (PHEVs). The high-level Pareto knowledge assists in finding a best compromise solution. The results of this article suggest that the energy and the comfort targets are harmonious, but both conflict with the safety target. Validation using real-world driving data shows that the Pareto optimum for CACC and EMS systems, relative to the baseline, can reduce energy consumption (by 7.57%) and tracking error (by 68.94%) while simultaneously satisfying ride comfort needs. In contrast to the weighted-sum method, the proposed Pareto method can optimally balance and scale the multiple-objective functions. In addition, sensitivity analysis proves that the vehicle reaction time impacts significantly on tracking safety, but its effect on energy saving is trivial.

Freeway vehicle fuel efficiency improvement via cooperative adaptive cruise control

Quantifying the effect of Cooperative Adaptive Cruise Control (CACC) on traffic mobility and vehicle fuel consumption has been a challenge because it requires a modeling framework that depicts the interactions among manually driven vehicles and CACC vehicles in the complex multilane traffic stream. This study adopted a state-of-the-art traffic flow modeling framework to explore the impacts of CACC on vehicle fuel efficiency in mixed traffic. The analyses at a freeway merge bottleneck indicated that the CACC string operation resulted in a maximum of 20% reduction in energy consumption compared to the human driver only case. At 100% market penetration, CACC equipped vehicles consume 50% less fuel than adaptive cruise control (ACC) vehicles without vehicle-to-vehicle (V2V) communication and cooperation. This implied the importance of incorporating the V2V cooperation component into the automated speed control system. In addition, the CACC string operation could substantially improve freeway capacity without degrading the vehicle fuel efficiency. At 100% CACC market penetration, the capacity increased by 49% while the vehicle fuel consumption rate per vehicle mile traveled remained the same as the rate observed in the human driver only case. At lower CACC market penetrations, the vehicle fuel efficiency could be improved via using the dedicated CACC lane or implementing wireless connectivity on the manually driven vehicles. In the 40% CACC case, those strategies brought about 15% to 19% capacity increase without decreasing vehicle fuel efficiency. Those results highlighted the necessity of deploying CACC-specific operation strategies under lower CACC market penetrations.

A dynamic merge assistance method based on the concept of instantaneous virtual trajectory for vehicle-to-infrastructure connected vehicles

Lane-changing activities near freeway merging, diverging, and weaving sections are one of the major factors leading to recurrent bottleneck congestion. In recent years, microscopic dynamic merging assistance (DMA) methods are found efficient to improve mobility and safety in merging areas. The article proposes a solution, the connected vehicle (CV) vehicle-to-infrastructure (V2I)-based dynamic merge assistance (DMA) method, analyzing vehicle trajectory data collected at CV roadside units (RSU) and implements a “vehicle-gap” pairing process for vehicle-gap synchronized control. The vehicle-gap pairing is determined by dynamically predicting their merging potential per the relationship between the “instantaneous virtual trajectories” (IVTs) of mainline and onramp vehicles. The IVTs are generated according to the prevailing speed profiles on both ramp and mainline through lane. Other than those automated control-based methods, this article focuses on providing a realistic speed instruction to human drivers through CV communication. A Cooperative Adaptive Cruise Control model is adopted to calculate such speed instruction. The paired onramp/mainline vehicles who follow the speed instruction can form a putative platoon so that their speed and location can be synchronized all through the merging area to ensure the gap-opening and catching-up. The proposed method is evaluated within a simulation network based on the field data collected on Interstate 35 at Austin, TX. The proposed method is tested in a microscopic traffic simulation environment using VISSIM external driver model Application Programing Interface. The simulation results indicated significant reduced average travel time at merge sections during congestion and increased time-to-collision during merging compared with other existing microscopic merge control models.

A Hybrid Controller for DOS-Resilient String-Stable Vehicle Platoons

This paper deals with the design of resilient Cooperative Adaptive Cruise Control (CACC) for homogeneous vehicle platoons in which communication is vulnerable to Denial-of-Service (DOS) attacks. We consider DOS attacks as consecutive packet dropouts. We present a controller tuning procedure based on linear matrix inequalities (LMI) that maximizes the resiliency to DOS attacks, while guaranteeing performance and string stability. The design procedure returns controller gains and gives a lower bound on the maximum allowable number of successive packet dropouts. A numerical example is employed to illustrate the effectiveness of the proposed approach.

Modeling heterogeneous traffic with cooperative adaptive cruise control vehicles: A first-order macroscopic perspective

ABSTRACTThis paper proposes a modeling framework to characterize steady-state traffic flow relations for heterogeneous traffic composed of both standard (S) and Cooperative Adaptive Cruise Control (CACC, labeled C here) vehicles, capturing the impact of C market penetration and vehicle sequence within a lane. The resulting parameterized fundamental diagram is then integrated with a first-order macroscopic traffic model, allowing us to characterize the operational performance on a network for heterogeneous traffic with varying C market penetration rates. This approach is demonstrated through an illustrative case study which considers a small freeway section with time-varying demand, merging traffic from an entrance ramp, and C market penetration ranging from 0.0–1.0. The results indicate that maximum throughput does not change appreciably as C traffic is first introduced, but eventually increases significantly for mid-to-high C penetration rates. Additionally, it shows that increasing C market penetration and separating vehicle classes slows upstream congestion propagation.