Cooperative Adaptive Cruise Control Controller Research Articles

Analysis and Synthesis of Cooperative Adaptive Cruise Control Against the Hetero-Integration Poly-Net Loop Delays

Cooperative Adaptive Cruise Control (CACC) is a promising technology for improving the capacity and energy efficiency of the ground transportation system. In this paper, a novel CACC control scheme is proposed to deal with the adverse impacts of both inter- and intra-vehicle network delays. First, a hetero-integration poly-net (PN) loop delay analysis method is presented to clarify the system delays in CACC considering both inter- and intra-vehicle network influences. A mathematic equation is put forward to calculate the upper bound of the PN loop delays. Then a collaborative software-defined network scheme is presented to deal with the PN loop delays, which consists of the application/strategy, network-control and network-data planes. In the network-control plane, a fraction-type basic period scheduling method is adopted. In the application/strategy plane, a delay-tolerant model predictive controller is designed for decision-making while a combination of an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H_∞</i> controller and a linear quadratic regulator is adopted for acceleration tracking control against local intra-vehicle network delays. Finally, the proposed scheme is verified under a variety of scenarios based on comprehensive Hardware-in-the-Loop tests.

Platooning Cooperative Adaptive Cruise Control for Dynamic Performance and Energy Saving: A Comparative Study of Linear Quadratic and Reinforcement Learning-Based Controllers

In recent decades, the automotive industry has moved towards the development of advanced driver assistance systems to enhance the comfort, safety, and energy saving of road vehicles. The increasing connection and communication between vehicles (V2V) and infrastructure (V2I) enables further opportunities for their optimisation and allows for additional features. Among others, vehicle platooning is the coordinated control of a set of vehicles moving at a short distance, one behind the other, to minimise aerodynamic losses, and it represents a viable solution to reduce the energy consumption of freight transport. To achieve this aim, a new generation of adaptive cruise control is required, namely, cooperative adaptive cruise control (CACC). The present work aims to compare two CACC controllers applied to a platoon of heavy-duty electric trucks sharing the same linear spacing policy. A control technique based on reinforcement learning (RL) algorithm, with a deep deterministic policy gradient, and a classic linear quadratic control (LQC) are investigated. The comparative analysis of the two controllers evaluates the ability to track inter-vehicle distance and vehicle speed references during a standard driving cycle, the string stability, and the transient response when an unexpected obstacle occurs. Several performance indices (i.e., acceleration and jerk, battery state of charge, and energy consumption) are introduced as metrics to highlight the differences. By appropriately selecting the reward function of the RL algorithm, the analysed controllers achieve similar goals in terms of platoon dynamics, energy consumption, and string stability.

V2V‐enabled cooperative adaptive cruise control strategy for improving driving safety and travel efficiency of semi‐automated vehicle fleet

AbstractAlthough highly automated vehicle fleets have committed a safe and efficient traffic flow, the next few years are likely the human‐driven vehicles (HDV) and intelligent vehicles (IV) mixed in large‐scale traffic network, where the interaction of HDVs with IVs will become more frequent. In this study, the authors investigate the traffic flow performance in a widespread of V2V environment deployed for all types of HDVs and IVs, levelled from L0 to L5. Based on this assumption, the vehicle movements and operations are collected and assembled into a V2X BSM dataset, and to any vehicle in fleet, the sequential movements and driving behaviours of the vehicle ahead can be predicted in the short term and considered as the input to cooperative adaptive cruise control (CACC) model of the controlled vehicle. The predicted motion of the vehicle ahead is applied to rolling optimization process of a given MPC framework, which calculates appropriate longitudinal operation for each car‐following vehicle and keeps optimal equilibrium state of the vehicle fleets in heterogeneous traffic flow. The experimental results showed that when the front vehicle generates random acceleration/deceleration in saturated and over saturated traffic state, in the car‐following case, the improved CACC controller maintains average car‐following distance at 20–40 m, the acceleration variation in the range [−0.1, 0.25], the gap error changed within [−10, 13], and controls the relative speed variation from −0.25 to 0.3 m/s, compared to an average headway of 20–140 m, acceleration variation in the range [−1.6, 0.65], gap error changed within [−21, 30], and a relative speed variation from −1.75 to 4.1 m/s by the traditional CACC controller. In the car‐in and car‐out cases, the vehicle controlled by the improved CACC controller had a travel displacement of 4050 m, a speed fluctuation interval of [18.7, 27.7], and an acceleration variation range of [−0.15, 0.2], while the vehicle controlled by the traditional MPC controller had a travel displacement of 3800 m, a speed fluctuation interval of [13.8,27.7] and an acceleration variation range of [−2.9, 0.6]. The improved CACC model can adapt more accurately and quickly to the maneouvre of vehicle ahead in V2V environment, which can effectively improve the capacity of heterogeneous traffic flow and the safety and comfort of driving.

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

Longitudinal vehicle control models, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), have been the core component of many Automated Driving Systems (ADS) or Advanced Driver Assistance System (ADAS). ACC and CACC systems make vehicles drive with faster reaction time and smaller following distance than manual vehicles, alleviating the congestions and attenuating traffic disturbances. Many existing ACC and CACC control models focus on three strategies that can indirectly mitigate congestions and damp traffic shockwaves: time headway suppression, stability improvement, and adaptive mode-switching control based on traffic conditions. Although congestion mitigation and shockwave damping are achievable with those control strategies, the improvement is often limited to the by-product effect of their main control objectives of headway suppression and system stability. Many adaptive models also need to calibrate many parameters for different road conditions.This paper proposes a new ACC and CACC framework that directly integrates the congestion shockwave damping and traffic congestion mitigation into the objective function of a dynamic control framework. A Model Predictive Control (MPC) based dynamic optimization framework is proposed to balance the trade-offs among multiple objectives, including safety, efficiency, shockwave, elasticity, and driver’s comfort. The proposed framework is applied in both ACC and CACC platoons mixed with manual vehicles, and five different control models are explored. The stability conditions of the proposed models are derived analytically and analyzed numerically. Three experimental studies, including platoon, ring-road, and traffic simulation studies, indicate promising results of proposed models in reducing shockwave propagation speed and mitigating traffic congestion under different environments compared with existing ACC and CACC models.

Memory-Anticipation Strategy to Compensate for Communication and Actuation Delays for Strings-Stable Platooning

Cooperative Adaptive Cruise Control (CACC) has the potential to significantly improve traffic throughput by reducing inter-vehicle following gap. However, communication and actuation delays in CACC systems are detrimental to string stability. A much larger time gap than the lump sum of the two delays is often required for CACC to guarantee string stability, which compromises their benefits on traffic throughput. It remains as a challenge to design string-stable CACC controllers that operate at small time gaps under delays. In this paper, we design a memory-anticipation CACC to explicitly handle communication and actuation delays to fulfill string stability specifications. The proposed controller employs a novel memory-anticipation spacing policy to address communication and actuation delays and is applicable to heterogeneous platoons. Unlike existing CACC controllers, our proposal utilizes past information of the preceding vehicle (i.e. the memory) instead of its current state/control information, which makes the controller robust to communication delay. Furthermore, a Smith predictor (i.e. the anticipation) is designed to compensate for the actuation delay. Both individual vehicle stability and string stability can be guaranteed with the minimum time gap as small as the summation of communication delay and actuation delay. Controller analysis and simulation verify the enhancement of the stability performance with respect to existing studies.

Cooperative Adaptive Cruise Control for Heterogeneous Platoons with Actuator Delay

Cooperative adaptive cruise control (CACC) is a control method that enables close vehicle following with string stable behavior, i.e., disturbances are not amplified through the vehicle string. Consequently, the technique contributes to increased road throughput and safety. General practice in CACC design is to describe the longitudinal vehicle dynamics by a simple first order model. For certain vehicles, however, this model has to be extended by an input delay to better represent the longitudinal behavior. In this paper, we show that the performance of a class of heterogeneous CACC controllers greatly deteriorates when the ego vehicle has a delay in the driveline. The minimum required string stable timegap increases to such a degree, that the controller is no longer suitable for close vehicle following. By implementing a Smith predictor in the control scheme, the delay is compensated, restoring the string stability properties. However, the original vehicle following objective is no longer fulfilled with the inclusion of the Smith predictor. By adopting an alternative timegap, the original control objective is restored for steady state situations, enabling CACC for heterogeneous platoons with actuator delays.

Data‐iteration cooperative adaptive cruise control of mixed heterogeneous vehicle platoons with unknown dynamic characteristics

AbstractThis paper considers the cooperative adaptive cruise control (CACC) problem of mixed heterogeneous vehicle platoons composed of human‐driven and CACC vehicles with unknown dynamic characteristics and an alternative to CACC for platooning of the heterogeneous vehicle platoon is presented. Adaptive dynamic programming is firstly used to learn the dynamic characteristics of acceleration of vehicles from the sampled data. Then the data‐iteration optimal CACC controller is computed to ensure that each CACC vehicle can reach a desired inter‐vehicle distance and desired common velocity with no prior knowledge of the dynamics of vehicles in the mixed platoon. Moreover, the string stability of the mixed vehicle platoon is derived by establishing some sufficient conditions on the acceleration transfer function of adjacent vehicles. Two simulation experiments of a six‐vehicle mixed platoon are used to illustrate the effectiveness of the proposed CACC method.

Longitudinal deep truck: Deep longitudinal model with application to sim2real deep reinforcement learning for heavy‐duty truck control in the field

AbstractTo develop cooperative adaptive cruise control (CACC), the choice of control approach often influences and can limit the choice of model structure, and vice versa. For heavy‐duty trucks, practical application of CACC in the field is heavily influenced by the accuracy of the used model. Deep learning and deep reinforcement learning (deep‐RL) have recently been used to demonstrate improved modeling and control performance for vehicles such as cars and quadrotors compared to state‐of‐the‐art. The literature on the application of deep learning and deep‐RL for heavy‐duty trucks in the field, which are significantly more complex than cars, is still sparse, however. In this article, we develop a two‐layer gray‐box deep learning model to capture longitudinal dynamics of heavy‐duty trucks while abstracting their complexity and present an approach to properly break the nested feedback loops in the model for training. We compare this model with three other alternative models and show that it achieves better general performance compared to a standard artificial neural network and results in and slower steady‐state acceleration and speed error growth rates, respectively. We then present an architecture to utilize these deep learning models within the deep‐RL framework and use it to develop baseline CACC controllers that can be zero‐shot transferred to the field. To carry out the work, we present a setup of differently configured trucks along with their interface architecture and stochastic driving cycle generators for data collection. Numerical validation of the approach demonstrated stationary and bounded modeling error, and demonstrated transfer of CACC controllers with consistent overshoot bounds and a stable approximately‐zero steady‐state error. Validation from field experiments demonstrated similarly consistent results. Compared to a state‐of‐the‐art benchmark, the deep‐RL controller achieved lower speed and time‐gap error variance but higher time‐gap error offset.

Cut through traffic like a snake: cooperative adaptive cruise control with successive platoon lane-change capability

A cooperative adaptive cruise control (CACC) system may be impeded by slow-moving traffic in the application. To improve the mobility of CACC, this research proposes a CACC controller with successive platoon lane-change capability. The goal is to help a platoon cut through traffic successively like a snake via smaller windows. The proposed controller has the following features: i) with successive platoon lane-change capability; ii) with string stability and lateral stability; iii) with consideration of vehicle dynamics. The proposed controller is evaluated on a simulation platform with the context of traffic and a joint simulation platform consisting of PreScan and Matlab/Simulink. the Results demonstrate that compared to the conventional controller: i) platoon lane-change competence is enhanced by 71.36% on arterials and 120.49% on freeways; ii) platoon lane-change efficiency is enhanced by 25.05% on arterials and 41.36% on freeways; iii) the proposed controller is more robust against congestion. Moreover, the computation time of the proposed controller is approximately 15 milliseconds when running on a laptop equipped with an Intel i7-8750H CPU. This indicates that the proposed controller is ready for real-time implementation.

A local traffic characteristic based dynamic gains tuning algorithm for cooperative adaptive cruise control considering wireless communication delay

Due to the inevitable existence of wireless communication delay, the string stability of cooperative adaptive cruise control (CACC) platoon systems may not be guaranteed if the controller gains are not tuned in time. Most relevant studies discussed how to determine the stable region of controller gains under different communication delays, but seldom told us how to select the optimal gains. Some simulation-based approaches just gave out the feasible gains to meet CACC string stability conditions under different communication delays by using standard input signal (e.g. Sine wave, step function) as the leading vehicle acceleration curve. Unfortunately, as the above approaches did not consider the local traffic characteristics, these feasible gains were not robust enough and even failed when tackling a realistic environment. To this end, this paper proposes a dynamic controller gains tuning algorithm to ensure CACC string stability. First, a synthesized leading vehicle acceleration curve is obtained by performing an inverse fast Fourier transform (IFFT) on the average frequency spectra of 230 vehicles’ acceleration from the next-generation simulation (NGSIM) dataset, which is highly related to the local traffic flow characteristics of the specified road. Then, the ratio of the ∞-norm of the spacing error between the ego vehicle and the preceding vehicle is set as the objective function, and with the synthesized curve as the input of the vehicle platoon, we apply an improved particle swarm optimization (PSO) algorithm to dynamically tune the gains of the CACC controller when the wireless communication delay changes. Hereby, the improved PSO utilizes the nonlinear decreasing inertia weight to accelerate the convergence speed of the algorithm. Finally, three groups of detailed simulation experiments are conducted to verify the effectiveness and performance of the dynamic optimal gains generated by the synthesized curve on string stability and assess the convergence speed of the improved PSO. The experimental results reveal that the proposed algorithm outperforms the traditional methods and can significantly suppress the disturbance along the direction upstream of the platoon.

Impact of Cooperative Adaptive Cruise Control on Traffic Stability

Cooperative adaptive cruise control (CACC) is one of the popular connected and automated vehicle (CAV) applications for cooperative driving automation with combined connectivity and automation technologies to improve string stability. This study aimed to derive the string stability conditions of a CACC controller and analyze the impacts of CACC on string stability for both a fleet of homogeneous CAVs and for heterogeneous traffic with human-driven vehicles (HDVs), connected vehicles (CVs) with connectivity technologies only, and autonomous vehicles (AVs) with automation technologies only. We mathematically analyzed the impact of CACC on string stability for both homogeneous and heterogeneous traffic flow. We adopted parameters from literature for HDVs, CVs, and AVs for the heterogeneous traffic case. We found there was a minimum constant time headway required for each parameter design to ensure stability in homogeneous CACC traffic. In addition, the constant time headway and the length of control time interval had positive correlation with stability, but the control parameter had a negative correlation with stability. The numerical analysis also showed that CACC vehicles could maintain string stability better than CVs and AVs under low HDV market penetration rates for the mixed traffic case.

Real-Time Distributed Cooperative Adaptive Cruise Control Model Considering Time Delays and Actuator Lag

Real-time control of a fleet of Connected and Automated Vehicles (CAV) for Cooperative Adaptive Cruise Control (CACC) is a challenging problem concerning time delays (from sensing, communication, and computation) and actuator lag. This paper proposes a real-time predictive distributed CACC control framework that addresses time delays and actuator lag issues in the real-time networked control systems. We first formulate a Kalman Filter-based real-time current driving state prediction model to provide more accurate initial conditions for the distributed CACC controller by compensating time delays using sensing data from multi-rate onboard sensors (e.g., Radar, GPS, wheel speed, and accelerometer), and status-sharing and intent-sharing data in BSM via V2V communication. We solve the prediction model using a sequential Kalman Filter update process for multi-rate sensing data to improve computational efficiency. We propose a real-time distributed MPC-based CACC controller with actuator lag and intent-sharing information for each CAV with the delay-compensated predicted current driving states as initial conditions. We implement the real-time predictive distributed CACC control algorithms and conduct numerical analyses to demonstrate the benefits of intent-sharing-based distributed computing, delay compensation, and actuator lag consideration on string stability under various traffic dynamics.

Bidirectional asymmetric delay feedback for cooperative adaptive cruise control of vehicle platoons with unreliable communication

AbstractThis paper considers the cooperative adaptive cruise control (CACC) problem of vehicle platoons under unreliable vehicle‐to‐everything (V2X) communication network. A bidirectional asymmetric delay feedback CACC method is presented to cope with the time delay and packet dropout induced by the unreliable network. The concept of equivalent delays is used to compensate the packet dropout of the network with the bidirectional communication topology. Using the asymmetric PD structure and the longitudinal models with varying time delay, the CACC controller of the platoon in a centralized mode is computed by solving linear matrix inequalities. To guarantee string stability of the platoon with the controller under the unreliable network, some sufficient conditions on the parameters of the controller and the time delay and packet dropout are derived. Furthermore, the upper bound of the time delay is estimated to satisfy string stability of the platoon. Finally, the presented method is verified by some comparative experiments of a seven‐car platoon with representative traffic scenarios and communication topologies.

Development of a Robust Cooperative Adaptive Cruise Control With Dynamic Topology

This research proposed a robust Cooperative Adaptive Cruise Control (CACC) to overcome the shortcoming of the existing CACC controllers in dealing with unexpected events, such as malware attack, phishing attack, and DNS tunneling attack, where perception and data received via communication contradict with the reality. The proposed controller combines the advantage of two information flow topologies – All-Predecessor Following (APF) and Predecessor-Leader Following (PLF) control methods – to improve the capability of CACC platoons. The string stability of the proposed CACC controller was proven. The robustness to time delay switch (TDS) attacks was assessed using simulation. The normal cruise was simulated to show the capability of the proposed controller in a wide range of speeds. TDS attacks, which encompassed four different unexpected events, were tested to verify the robustness of the proposed CACC. Results confirmed that the proposed CACC controller is string stable and robust against these TDS attacks without crashing or causing jerks. It also showed that the proposed CACC controller outperformed a state-of-the-art CACC controller.

A generic simulation platform for cooperative adaptive cruise control under partially connected and automated environment

Cooperative Adaptive Cruise Control (CACC) has been regarded as the most promising technology that can be carried out in field earliest under partially connected and automated environment, and is one of the few Connected and Automated Vehicle (CAV) applications closest to its final shape. Therefore, validation of CACC running in real traffic is in urgent need. To support the validation, simulation plays a key role. This paper proposes a next-generation simulation platform for CACC evaluation and validation. It is able to not only validate the performance of a CACC platoon by itself, but also evaluate its performance when being released into the traffic. In addition, it can also quantify the impact of CACC vehicles on transportation systems. The proposed platform is generic in terms of CACC control system, background traffic condition, road geometry, and traffic control scheme. Furthermore, it is capable of capturing the dynamics of platoon formation and disengagement when CACC equipped vehicles are mixed up with human-driven vehicles. The proposed platform is validated by comparing against actual field data and previously published theoretical studies. The results confirm the credibility of the proposed platform in terms of: i) CACC control system embedded; ii) CAV and Human-driven Vehicle (HV) mixed traffic simulation; iii) managed lane simulation. It is also revealed that current CACC technology without upgrade may be a safety hazard and permitting CACC only on a dedicated CACC lane might be a good first step for CACC commercialization at this moment.

Safety Analysis of a Modified Cooperative Adaptive Cruise Control Algorithm Accounting for Communication Delay

Cooperative adaptive cruise control (CACC) is a promising technology to improve traffic efficiency and enhance road safety. In this paper, a modified CACC control model considering the communication time delay is proposed, which is used to investigate the longitudinal safety impacts of the communication time delay to the CACC platoon. Then, the communication time delay model is integrated into the CACC model to simulate the realistic information transfer process in the CACC platoon. Then a microscopic CACC platoon simulation is designed and conducted to verify the feasibility and reliability of the modified CACC control algorithm. The obtained results reveal that the modified CACC control algorithm can not only reduce about 96.6% of inter-vehicle spacing error, but also enhance the vehicles’ ability to sense the upstream traffic changes. Furthermore, to quantitatively analyze the longitudinal safety influence of the time delay caused by representative communication systems, sensitivity analysis experiments of headway time were designed and conducted. In the sensitivity analysis, the time exposed time-to-collision (TET) and the time-integrated time-to-collision (TIT) were introduced as the key performance indicators (KPIs) to quantify the rear-end collision risks. Sensitivity analysis results demonstrate that the performance of the CACC platoon is strictly related to the applied wireless communication style. Furthermore, the CACC system supported by the 5th generation (5G) communication system shows great advantages in narrowing the minimal headway time gap and reducing the rear-end collision risks.

Closed-loop platoon simulation with cooperative intelligent transportation systems based on vehicle-to-X communication

The continuous enhancement of X-in-the-Loop (XiL) simulation methods is one key to efficiently test advanced vehicle control functions and ensure high software quality under a rising cost and time pressure. Systems, which control vehicles based on sensor perception can already be evaluated in XiL simulations. However, future vehicles will not exclusively represent stand-alone systems anymore, but involve Cooperative Intelligent Transport Systems (C-ITS), distributed among different traffic participants. While several simulation environments for C-ITS feasibility studies are available, common virtual test frameworks cannot incorporate multiple interacting and communicating road users with the required fidelity to secure C-ITS software, e.g. for platooning applications.Within the scope of this publication, a method for virtual testing and calibration of in-vehicle C-ITS using a high fidelity vehicle platoon model is presented. Multiple closed-loop controlled vehicles and the mutual interferences between them are simulated in a common 3D environment alongside other traffic. Each high fidelity vehicle model individually simulates the longitudinal and lateral response, the generation of sensor data plus the exchange of vehicle-to-vehicle data. As a proof of concept, a model-in-the-loop simulation for a Cooperative Adaptive Cruise Control (CACC) was conducted. The simulation environment included five high fidelity vehicle models forming a platoon, coupled with CACC controller models under test.The results reveal that the developed simulation environment captures the interaction within collaborative vehicle groups while modeling individual vehicles as controlled systems in the same tool. At the same time the method satisfies all requirements for XiL testing. Functional tests of multiple interacting vehicle controllers have been successfully carried out. In the performed feasibility study simulation-based tests uncovered an insufficient control stability of the CACC. Upcoming series developments and homologations of in-vehicle C-ITS software and hardware can be supported by the new virtual test environment to overcome connectivity, reproducibility and cost efficiency limitations of physical test environments. Next steps include proving the method in use for series development and extending it to cover vehicle-to-infrastructure use cases.

Data-driven optimal cooperative adaptive cruise control of heterogeneous vehicle platoons with unknown dynamics

This paper considers the cooperative adaptive cruise control (CACC) problem of heterogeneous vehicle platoons and proposes a data-driven optimal CACC approach for the heterogeneous platoon with unknown dynamics. To cope with the unknown dynamics of the vehicle CACC platoon system, the adaptive dynamic programming is used to design an online iteration policy for optimal CACC of the platoon. Using the predecessor-following topology, the CACC controllers are computed by employing the desired spacing errors, relative velocities, and accelerations of the vehicles. The stability of the closed-loop CACC system and the iteration algorithm are presented. Furthermore, the string stability of the platoon with the CACC system is established in terms of the acceleration transfer function between adjacent vehicles in frequent domain. Finally, the effectiveness of the proposed method is verified in two complex scenarios of varying speed cruise.

Stability Design for the Homogeneous Platoon with Communication Time Delay

A hierarchical control framework is applied for the distributed cooperative vehicular platoon using vehicular ad-hoc networks. The parameter-space-approach-based cooperative adaptive cruise control (CACC) controller is proposed to guarantee the {mathcal{D}}-stability and the string stability considering the influence of the communication time delay and time lag of vehicular dynamic performance. This CACC controller combines the feedforward loop of the acceleration of the preceding vehicle with the feedback loop of the following errors, in which the gain of the feedforward loop is designed to decrease matching errors and the gains of the feedback loop are selected from the feasible region in the parameter space. To verify the effectiveness of the CACC controller, a six-vehicle platoon with a simplified vehicular dynamic is simulated under speed-up and stop scenarios. The simulation results demonstrate that the disturbance is attenuated along with the platoon and the following errors are convergent with well-designed convergent performance. A CarSim/Simulink co-simulation is designed to further verify the effectiveness of the hierarchical control framework and the rationality of the CACC controller in the real vehicular platoon application. The simulation results under the highway fuel economy test drive cycle show that the CACC controller improves the drive comfort and significantly decreases the following errors.

Impact of Mixed Traffic on the Energy Savings of a Truck Platoon

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;A two-truck platoon based on a prototype cooperative adaptive cruise control (CACC) system was tested on a closed test track in a variety of realistic traffic and transient operating scenarios - conditions that truck platoons are likely to face on real highways. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate, serving as proxies to evaluate the impact of aerodynamic drag reduction under constant-speed conditions. These measurements demonstrate the effects of: the presence of a multiple-passenger-vehicle pattern ahead of and adjacent to the platoon, cut-in and cut-out manoeuvres by other vehicles, transient traffic, the use of mismatched platooned vehicles (van trailer mixed with flatbed trailer), and the platoon following another truck with adaptive cruise control (ACC). These scenarios are intended to address the possibility of “background aerodynamic platooning” impacting realized savings on public roads. Using calibrated J1939 fuel rate analysis, fuel savings for curved track sections versus straight track sections were also evaluated for these scenarios, highlighting differences in the implementation of the CACC control strategies compared to a stock ACC implementation. The use of different trailer types and the presence of passenger-vehicle traffic patterns showed a measurable impact on platoon performance in some conditions, but the basic fuel savings trends were retained.&lt;/div&gt;&lt;/div&gt;