Nonlinear MPC for Emission Efficient Cooperative Adaptive Cruise Control

Model predictive control policy design, solutions, and stability analysis for longitudinal vehicle control considering shockwave damping

This paper proposes an optimal design method for the adaptive cruise control model to enhance the string stability with the adaptive cruise control (ACC). First, the influence of control gain parameters on ACC and cooperative adaptive cruise control (CACC) systems is analyzed from theoretical and numerical perspectives. Second, we compared the ACC and CACC models. On this basis, an optimal control gain parameter is proposed to consider the string stability of the ACC platoon system. Finally, we designed numerical simulation experiments to verify the effectiveness of the proposed ACC (PACC) model. Results show that compared with the classical ACC model, the PACC model has certain advantages in recovery time, vehicle average velocity, velocity standard deviation, and vehicle collision safety. Moreover, PACC is suitable for most equilibrium velocity scenarios, and it has good string stability with different time gaps, unlike the ACC and CACC models. As a result, the PACC model has better string stability and robustness. Therefore, the PACC model can enhance the string stability and provide theoretical support for designing better ACC systems.

The incorporation of a macroscopic approach reflecting Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) traffic dynamics in a gas-kinetic (GKT) traffic flow model is presented. The approach is a novel one and is based on the introduction of a relaxation term that satisfies the time/space-gap principle of ACC or CACC systems. The relaxation time is assigned on multiple leading vehicles in the CACC case, whereas in the ACC case this relaxation time is only assigned to the direct leading vehicle. Numerical simulations investigate the effect of ACC and CACC to traffic flow macroscopic stability with respect to perturbations introduced in a ring road and to flow characteristics in open freeways with merging flows at an on-ramp. Following from the results, it can be deduced that CACC vehicles increase the stabilization of traffic flow, compared to ACC ones. Further, the proposed CACC approach can further improve the dynamic equilibrium capacity and traffic dynamics, especially at on-ramp bottlenecks.

<div class="section abstract"><div class="htmlview paragraph">Platooning is a promising technology which can mitigate greenhouse gas impacts and reduce transportation energy consumption. Platooning is a coordinated driving strategy where trucks align themselves in order to realize aerodynamic benefits to reduce required motive force. The aerodynamic benefit is seen as either a “pull” effect experienced by the following vehicles or a “push” effect experienced by the leader. The energy savings magnitude increases nonlinearly as headway (following distance) is reduced [<span class="xref">1</span>]. In efforts to maximize energy savings, cooperative adaptive cruise control (CACC) is utilized to maintain relatively short headways. However, when platooning is attempted in the real world, small transient accelerations caused by imperfect control result in observed energy savings being less than expected values.</div><div class="htmlview paragraph">This study analyzes the performance of a recently developed nonlinear model predictive control (NMPC) platooning strategy over challenging terrain. The NMPC strategy is compared to the previous proportional-integral-derivative (PID) control scheme in terms of headway, commanded torque, and fuel rate variances along with the total fuel consumed per lap. These comparisons reveal that the NMPC based controller’s ability to optimize headway variation while considering upcoming grade disturbances reduces the harshness of commanded torque and fuel rate transients. These platoon behavior changes result in significant fuel energy consumption reductions. In all platooning configurations analyzed, the NMPC strategy consumed less fuel than the comparable PID based data. This is best exemplified by findings from platoons with increased headway spacing. When compared to PID platoon control, the NMPC produced 25.5% and 31.6% fuel consumption decreases for the final truck in four-truck platoon configurations when targeting 50 foot and 100 foot follow distances, respectively. These results suggest that the NMPC implementation minimizes extraneous acceleration events associated with rigid PID headway adherence.</div></div>

Nonlinear Model Predictive Control approach in design of Adaptive Cruise Control with automated switching to cruise control

A macroscopic approach modeling the penetration rate of Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) vehicles and its effect on traffic dynamics is investigated. Modeling is based on the introduction of a relaxation term in a gas-kinetic traffic flow (GKT) model that satisfies the time-gap principle of ACC or CACC systems and allows for consideration of mixed traffic of manual and ACC/CACC vehicles. The relaxation time is assigned to multiple leading vehicles in the CACC case; whereas in the ACC case only relates to the direct leading vehicle. Numerical simulations investigate the effect of the penetration rates of ACC and CACC equipped vehicles to traffic flow macroscopic stability, with respect to perturbations introduced in a ring road, and to flow characteristics in an open freeway with merging flow at an on-ramp.

Emerging developments in the field of automotive technologies, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) systems, are expected to enhance traffic efficiency and safety on highways and urban roads. For this reason, substantial effort has been made by researchers to model and simulate these automation systems over the last few years. This study aims to integrate a recently developed car-following model for ACC and CACC equipped vehicles in the microscopic traffic simulation tool SUMO; the implemented ACC/CACC simulation models originate from empirical ones, ensuring the collision-free property in the full-speed-range operation. Simulation experiments for different penetration rates of cooperative automated vehicles, desired time-gap settings and network topologies are conducted to test the validity of the proposed approach and to assess the impact of ACC and CACC equipped vehicles on traffic flow characteristics.

PACC: A platoon-based adaptive cruise control strategy based on leader-following information topology to mitigate traffic oscillations under CAV environment

With the rise of Connected-and-Automated-Vehicle (CAV) technologies on roadways, transportation networks have become increasingly connected through Vehicle-to-Everything (V2X) systems. With access to the additional data from V2X, modern cruise control systems like Adaptive Cruise Control (ACC) are further improved upon to develop systems like Cooperative ACC (CACC) which reduces traffic congestion and increases driver safety and energy efficiency. With that increased connectivity, previously closed vehicle systems are now vulnerable to new security threats which pose new technical challenges. Significant research has been done to strengthen the network against external threats such as denial-of-service attacks (DoS) or passive eavesdropping attacks using network management and cryptographic strategies. Internal threats like data falsification are more challenging to address because they originate from already authenticated sources on the network. This work suggests a method to locally determine if network data can be trusted utilizing only the intra-vehicle sensors against the network data. It functions by leveraging the synchronization of CACC vehicle stream members to identify potentially malicious data. In the event the network data is determined to be untrustworthy, the vehicle will change its mode of operation to basic ACC where it will disconnect from the vehicle stream and increase the distance from the preceding vehicle. In order to test this approach, an ACC system was created and then modified into a simple CACC system that includes the V2X network data streams. Two common V2X applications were used to show the functionality of both the simple CACC system and the work: a Vehicle-to-Infrastructure (V2I) enabled traffic light and a Vehicle-to-Vehicle (V2V) vehicle stream.

Much of the existing research on drivers’ understanding of adaptive cruise control (ACC), a type of advanced driver assistance system, was conducted several years ago. Through an online survey, this study aimed to assess ACC knowledge among ACC owners and non-owners now that this system is more widely available. Along with knowledge of ACC features and limitations, demographic information, experience with technology, and experience with ACC (for owners) were also collected to investigate which factors predicted knowledge of ACC features and limitations. Results showed that owners today may have a better understanding of some of the main limitations of ACC compared with research conducted over 10 years ago. However, a large percentage of owners still had misperceptions about their ACC system. While owners had a slightly higher percentage of correct answers overall, they did not differ from non-owners in their knowledge of limitations. As this technology is becoming more common, even non-owners may be becoming aware of common limitations; owning and using ACC does not seem to result in a better system understanding. Higher income was associated with a higher percentage of correct responses on the ACC knowledge questionnaire for both owners and non-owners, and for non-owners, higher education level was also significantly associated with a higher percentage of correct responses. Future research should focus on developing training materials that are accessible to all drivers, so that drivers in lower education and income groups are also supported to understand how advanced driver assistance systems work and benefit from these technologies.

The paper presents a curving adaptive cruise control (ACC) system that is coordinated with a direct yaw-moment control (DYC) system and gives consideration to both longitudinal car-following capability and lateral stability on curved roads. A model including vehicle longitudinal and lateral dynamics is built first, which is as discrete as the predictive model of the system controller. Then, a cost function is determined to reflect the contradictions between vehicle longitudinal and lateral dynamics. Meanwhile, some I/O constraints are formulated with a driver permissible longitudinal car-following range and the road adhesion condition. After that, desired longitudinal acceleration and desired yaw moment are obtained by a linear matrix inequality based robust constrained state feedback method. Finally, driver-in-the-loop tests on a driving simulator are conducted and the results show that the developed control system provides significant benefits in weakening the impact of DYC on ACC longitudinal car-following capability while also improving lateral stability.

The effects on traffic flow of increasing proportions of both autonomous and cooperative adaptive cruise control (ACC) vehicles relative to manually driven vehicles were studied. Such effects are difficult to estimate from field tests on highways because of the low market penetration of ACC systems. The research approach used Monte Carlo simulations based on detailed models presented in the literature to estimate the quantitative effects of varying the proportions of vehicle control types on lane capacity. The results of this study can help to provide realistic estimates of the effects of the introduction of ACC to the vehicle fleet. Transportation system managers can recognize that the autonomous ACC systems now entering the market are unlikely to have significant positive or negative effects on traffic flow. An additional value of studying ACC systems in this way is that these scenarios can represent the first steps in a deployment sequence that will lead to an automated highway system. Benefits gained at the early stages in this sequence, particularly through the introduction of cooperative ACC with priority access to designated (although not necessarily dedicated) lanes, can help support further investment in and development of automated highway systems.

In this paper, we provide a systematic procedure for designing cooperative adaptive cruise control (CACC) systems that are robust to parasitic actuation lag in braking and propulsion and delay in the communicated acceleration from the predecessor vehicle. In particular, we derive a tight lower bound on the employable time headway in CACC systems for guaranteeing robust string stability. The lower bound on the time headway is dependent on the upper bound on the parasitic actuation lag (τ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>) and the communication delay (ℓ). The main result of the paper is that if τ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> exceeds ℓ, then the employable time headway is lower bounded by τ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>+ℓ. Otherwise, it is better to use adaptive cruise control (ACC) where the employable time headway is lower bounded by 2τ<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub>. The above results on CACC systems are then extended to next-generation CACC (CACC+) systems that employ information from multiple predecessor vehicles. Several comparative numerical simulations for a representative maneuver corroborate the main results.

Evaluating impacts of different longitudinal driver assistance systems on reducing multi-vehicle rear-end crashes during small-scale inclement weather

In the Netherlands, a field operational test was conducted in 2006 to assess the impact of two advanced driver assistance systems (ADAS), namely adaptive cruise control (ACC) and lane departure warning (LDW) systems. The research goal was to estimate the effects of these systems on road capacity, safety and emissions. In this paper we focus on the interaction between driver and ACC system using the data from this FOT. It is found that drivers choose headway settings according to their manual driving behavior. Moreover, they often keep the system deactivated under dense traffic conditions. It is also observed from the data that the system, once de-activated, either automatically or manually, needs some time to become again active. These findings imply that, even with 100% cars on the road equipped with ACC, manual driving behavior will still be a determinant factor.