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This study focuses on how to improve the merge control prior to lane reduction points due to either accidents or constructions. A Cooperative longitudinal Control for Merging maneuvers (CCM) strategy based on Automated Vehicles (AV) is proposed considering cooperation among vehicles, courtesy, and the coexistence of AV and Human-Driven Vehicles (HDV). CCM introduces a modified/generalized Cooperative Adaptive Cruise Control (CACC) for vehicle longitudinal control prior to lane reduction points. It also takes courtesy into account to ensure that AV behave responsibly and ethically. CCM is evaluated using microscopic traffic simulation and compared with no control and CACC merge strategies. The results show that CCM consistently generates the lowest delays and highest throughputs approaching the theoretical capacity. Its safety benefits are also found to be significant based on vehicle trajectories and density maps. CCM mainly requires vehicles to have automated longitudinal (such as Adaptive Cruise Control (ACC)) and lane-changing control, which are already commercially available on some vehicles. Also, it does not need 100% AV penetration, presenting itself as a promising solution for improving traffic operations in lane reduction transition areas such as highway work zones. [ABSTRACT FROM AUTHOR]

• This paper has characterized the empirical car-following (CF) behaviors of a commercial ACC system and explained the underlying mechanism(s). • The ACC behaviors (i.e., response time, oscillation growth, and overshoot) largely depend on the ACC headway, speed level, and leader stimulus. • In a platoon, the oscillation growth progressively changes from one ACC to another, implying oscillation escalation and potential safety hazards. Emerging automated vehicle (AV) technologies are increasingly being deployed around the world and it is only a matter of time before the transportation landscape changes dramatically. Unfortunately, those changes cannot be well predicted due to the lack of empirical data. But adaptive cruise control (ACC) vehicles are common in the market and can be used to fill this gap. In this paper, we aim to characterize the empirical car-following behaviors of a commercial ACC system and understand how ACC behaves in different conditions and the underlying impact mechanism. It is found that for a single ACC: (i) the ACC response time is comparable to human drivers but much larger than the ACC controller time gap and it exhibits small variance, (ii) the ACC response can amplify or dampen an oscillation, (iii) after the oscillation, the stabilization process can exhibit overshooting or undershooting, and (iv) these CF behaviors depend largely on the ACC headway setting, speed level, and leader stimulus, which produce the impacts directly and/or indirectly through the mediation of earlier ACC behaviors. For a three-vehicle platoon, our main finding is that the change from one ACC vehicle to the next is progressive for oscillation growth, and regressive for deceleration, acceleration, and overshooting. This implies that in long platoons, oscillation amplitude tends to exacerbate very quickly, which forces ACC vehicles further upstream to apply very strong braking followed by a strong acceleration. This can cause significant overshooting and safety hazards. [ABSTRACT FROM AUTHOR]

This paper examines CACC truck platooning on uphill grades. It was found that the design of CT policy should consider the effects of low crawl speeds on significant upgrades. Three simple solutions, which have different impacts on traffic flow efficiency, are proposed. Furthermore, truck platoons, controlled by a state-of-the-art CACC model, become asymptotically unstable beyond some critical grade. The errors are permanent, suggesting that trucks fail to re-engage after the upgrade. This occurs by complex interactions between the CACC control and the bounded acceleration capabilities of trucks. New control concepts are developed to complement the existing control model and achieve asymptotic (and string) stability. The instability mechanisms and new control concepts are not specific to the control model used. [ABSTRACT FROM AUTHOR]

Experimental measurements on commercial adaptive cruise control (ACC) vehicles are becoming increasingly available from around the world, providing an unprecedented opportunity to study the traffic flow characteristics that arise from this technology. This paper adds new experimental evidence to this knowledge base and presents a comprehensive empirical study on the ACC equilibrium behaviors via the resulting fundamental diagrams. We find that, like human-driven vehicles, ACC systems display a linear equilibrium spacing-speed relationship (within the range of available data) but the key parameters of these relationships can differ significantly from human-driven traffic depending on input settings. At the minimum ACC headway setting, equilibrium capacities in excess of 3500 vehicles per hour are observed, together with an extremely fast equilibrium wave speed of 100 kilometers per hour. These fast waves are unfamiliar to human drivers, and may pose a safety risk. The results also suggest that ACC jam spacing can be much larger than in human traffic, which reduces the network storage capacity. • A comprehensive study on ACC fundamental diagrams based on worldwide experiments. • ACC has a linear speed-spacing relationship in the available data range. • ACC can produce high capacities excessing 3500 vph with fast congested wave speed. • ACC jam spacing is much larger than human, which reduces network storage capacity. [ABSTRACT FROM AUTHOR]

Currently, model predictive control (MPC) for adaptive cruise control (ACC) systems relies on the prediction of the leader's motion to plan the follower's trajectory. However, such predictions must be accurate to guarantee string stability, which represents an ongoing challenge for machine learning approaches. This issue can be circumvented by simply incorporating the leader's history, which follows from Newell's car-following (CF) model where a trajectory under congestion corresponds to a temporal–spatial shift of the leader's past trajectories. By leveraging this insight, this paper develops a family of MPC models based on Newell's CF model, labeled Newell MPCs, which are safe and can reduce traffic congestion. Specifically, We first present baseline Newell MPCs to replicate the original Newell's CF model, including the X b o u n d -Model, which uses the shifted leader trajectory as an upper envelope; and the X r e f -Model, which adopts the shifted leader trajectory as a reference to avoid the issue of infeasible solution triggered by hard constraints. To further improve the control performance, we propose the X V -Model which uses the leader speed history as an additional reference to enhance the model robustness and regulate speed over/under-shootings. In addition, we extend the single-leader Newell's model through incorporating multiple leaders and propose the X m u l -Model, which can achieve driver anticipation, and correspondingly reduce reaction time and improve string stability. Finally, based on the X V -Model, we present two additional extensions: (i) the X V r e l a x -Model, which incorporates driver relaxation to achieve smooth response to merging traffic; and (ii) the X V s s -Model, which achieves strict string stability to further dampen traffic oscillations. The proposed Newell MPCs are tested using both numerical simulations and field studies on a stock 2019 Honda Civic using Openpilot and Comma.ai; the source code is available at https://github.com/HaoZhouGT/openpilot. • A family of MPC ACCs based on Newell's CF model. • Leader history can outperform prediction for string stability. • Road test of Newell MPC on a production ACC car model. [ABSTRACT FROM AUTHOR]

Commercial adaptive cruise control (ACC) systems are bi-level: an upper-level planner decides the target trajectory and the low-level system executes it. Existing literature on ACCs mostly focus on the planner algorithms or the actuator delay, while the transition process between them, e.g. the low-level control design and its impact are often ignored. This paper tries to fill this gap by digging into the codebase of a recent open-source self-driving system, Openpilot (OP), Comma.ai, from which we extract and formulate the algorithms at both the upper and lower levels. For linear ACCs, the paper extends the transfer function analysis from planners only to full control loops and investigates the impact of slow/fast low-level control on the overall string stability (SS). For MPC ACCs, it studies their planning characteristics based its optimization objectives and approximates the low-level impact using an ODE approach. We find that low-level control has a significant impact on the overall SS of ACCs: (i) slow low-level control undermines SS under small frequencies and improves SS given large frequencies for linear systems, (ii) MPC features a varying gain throughout an oscillation, where the fast low-level control typically results in a 'fast-slow' changing process of the MPC gain, which benefits the SS, whereas the slow low-level control leads to a 'slow-fast' varying gain which undermines the SS, (iii) slow low-level control are common as they arise from comfort-oriented control gains, from a "weak" actuator performance or both, and (iv) the SS is very sensitive to the integral gain under slow low-level control for both PI and PIF controllers. Overall, the study recommends fast low-level control for ensuring vehicular SS to reduce traffic congestion, considering that large congestion waves usually feature both small frequencies and large amplitudes, although slow controllers could perform even better provided a short and small leader perturbation. The findings of this paper are verified both numerically and experimentally. For the first time in the literature we implement custom ACC algorithms on market cars, and achieve SS on open roads with a random leader by only tuning the low-level controllers. The source code is shared at https://github.com/HaoZhouGT/openpilot to support on-road experiments of arbitrary car-following models, which may be of interest to other studies. • Open-source ACC algorithms from Openpilot, Comma.ai with focus on low-level control. • Slow low-level control undermines string stability even if the planner is stable. • Slow low-level control can arise from control gains, gas/brake systems, or both. • A new experimental method to run custom ACCs on commercial vehicles. [ABSTRACT FROM AUTHOR]