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To 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 ~10x $\unicode{x0007E}10x$ better general performance compared to a standard artificial neural network and results in ~4x $\unicode{x0007E}4x$ and ~40x $\unicode{x0007E}40x$ 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. [ABSTRACT FROM AUTHOR]

Cooperative Adaptive Cruise Control (CACC), as an advanced version of adaptive cruise control (ACC), automates brake and engine controls based on the information received from wireless V2V communications and remote sensors, enabling smaller vehicle-following time gaps. It can improve the safety of vehicle platooning and increase fuel savings. As an extension of our previous investigation of truck drivers' acceptance of CACC, this case study investigates factors affecting the use of CACC for truck platooning. Nine commercial fleet drivers were recruited to operate two following trucks in a CACC-enabled string on freeways in Northern California. We analyzed the usage of CACC time gaps and its correlation with truck drivers' stated preferences for these time gaps, and we found that the highest preferred Gap 3 (1.2 s) was used the most. Moreover, a Bayesian regression model was built to show that truck drivers are more likely to disengage CACC when driving in low-speed traffic or on downgrades where this CACC could not provide sufficient braking. In high-speed traffic or on upgrades, truck drivers are more likely to engage CACC, particularly at Gap 3. Truck position, however, does not affect truck drivers' time gap selection. The findings encourage the adoption of CACC in the trucking industry through implementing driver-preferred time gaps and responsive braking systems, and operating on routes with minimal interference to truck speeds. [ABSTRACT FROM AUTHOR]

Recent studies have shown that positive impact of Cooperative Adaptive Cruise Control (CACC) can only be guaranteed as market penetration rate increases. Removing the string homogeneity constraint is essential to encourage widespread adoption. In this work, a hierarchical architecture is proposed to enable CACC on vehicles with not only mixed dynamics but also different powertrain types. A low-level layer deals with the vehicle and powertrain dynamics to provide accurate and consistent reference speed tracking response. The high-level layer uses: (1) a Linear Parameter Varying feedback system to provide loop stability, robustness and enforce a variable time gap policy and (2) a feedforward system that processes Vehicle-to-Vehicle information to enhance string stability and response bandwidth, by dealing with the string heterogeneity. A gap management strategy is built on top of the CACC architecture to handle gap setting changes or cut-in/out situations, via a dynamics constrained time gap trajectory planning algorithm. The proposed work has been designed, developed and validated on three different real passenger vehicles on public highways and test tracks, showing the potential of the proposed algorithm to enable robust string stable CACC, despite the different dynamics and powertrains considered. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Cooperative traffic signal control algorithm for enhancing the CACC vehicle string operation. • A robust system that operates under various CACC market penetrations and vehicle connectivity levels. • An algorithm that brings significant improvement of average vehicle speed and fuel efficiency at congested intersections. Vehicles equipped with Cooperative Adaptive Cruise Control (CACC) have the capability to broadcast their real-time speed and location information via wireless communications. They can also safely operate in multi-vehicle strings while keeping shorter than normal gaps among adjacent vehicles in the high-speed traffic stream. Such capabilities can greatly benefit the management of urban signalized intersections. In this study, we have developed a cooperative signal control algorithm that adopts the CACC datasets and the datasets collected by the traditional fixed traffic sensors to predict the future traffic conditions. The prediction allows the signal controller to assign signal priority to the intersection approach that accommodates the most CACC strings. Such a control strategy can significantly enhance the CACC string operation, which ultimately improves the overall intersection performance. The effectiveness of the algorithm has been tested in a simulated 4-way signalized intersection. The algorithm substantially outperforms the traditional actuated controller as it perceives the traffic flow more comprehensively and assigns the green time resource more efficiently than the traditional controller. Particularly, the average vehicle speed and the average vehicle miles travelled per gallon fuel consumed (MPG) can be increased by more than 10% when the CACC market penetration is 100%. In mixed traffic where CACC fleets frequently interact with manually driven vehicles, the algorithm is found to be more beneficial. The speed and MPG improvement exceed 30% when the CACC market penetration is 40%. The signal control algorithm can bring about significant benefit even when the CACC market penetration is 0%. In this case, it completely relies on the datasets obtained from the traditional traffic sensors. This finding demonstrates the robustness of the algorithm. It makes the proposed algorithm suitable to implement in real-world intersections under various CACC market penetrations and different levels of vehicle connectivity. [ABSTRACT FROM AUTHOR]

• Novel control design method for fractional-order controllers for more demanding design requirements. • A more graphical approach to tune fractional-order controllers, easing the control system deployment on any type of vehicles. • Application of such design approach to real world problems and real vehicles platforms. • A method to guarantee iso-damping properties, which is essential for automated car-following. This work deals with the control design and development of an automated car-following strategy that further increases robustness to vehicle dynamics uncertainties. The control algorithm is applied on a hierarchical architecture where high and low level control layers are designed for gap-control and desired acceleration tracking, respectively. A fractional-order controller is proposed due to its flexible frequency shape, fulfilling more demanding design requirements. The iso-damping loop property is sought, which yields a desired closed-loop stability that results invariant despite changes on the controlled plant gain. In addition, the graphical nature of the proposed design approach demonstrates its portability and applicability to any type of vehicle dynamics without complex reconfiguration. The algorithm benefits are validated in frequency and time domains, as well as through experiments on a real vehicle platform performing adaptive cruise control. [ABSTRACT FROM AUTHOR]

Highlights • Modeling framework for depicting the behaviors of CACC vehicles in mixed traffic. • Quantitative impact analysis of promising CACC string operation strategies. • Insights regarding the CACC performance at various freeway facilities. Abstract Modeling impacts of Cooperative Adaptive Cruise Control (CACC) on multi-lane freeway traffic can be challenging. It requires accurate description of the formation and disengagement of CACC vehicle strings when CACC vehicles are mixed with manually driven vehicles in the traffic stream. It also needs to depict the behaviors of CACC vehicles under the influence of CACC operation strategies such as the CACC vehicle managed lane (ML) and implementing the Vehicle Awareness Devices (VAD), which are intended to enhance the CACC string operations. To address these challenges, we extended a state of the art CACC modeling framework to incorporate new algorithms that are essential to describe the interactions among the CACC vehicles and manually driven vehicles in mixed traffic. The updated modeling framework adopts a new vehicle dispatching model to generate the high-volume traffic flow expected to exist due to the CACC string operation. The framework also includes new lane changing rules and automated speed control algorithms that ensure realistic CACC vehicle behaviors at freeway on/off-ramp areas where traffic disturbances might frequently interrupt the CACC string operations. With the model updates, we can further reproduce traffic flow dynamics under the influence of the CACC operation strategies. The modeling capability of the presented framework has been verified via case studies on a simple 4-lane freeway segment with an on-ramp and an off-ramp and a complex 18-kilometer freeway corridor. The case study results indicate that the presented modeling framework not only quantifies the mobility improvements for the study sites under different CACC market penetrations and CACC operation strategies, but also discloses the mechanism that governs the improvement. This study creates a methodology that can estimate detailed kinematics of connected automated vehicles under realistic traffic environments. Findings produced by the methodology are helpful for the future development, implementation and management of the advanced transportation technologies. [ABSTRACT FROM AUTHOR]