Signal Processing
Near-Field Wideband Channel Estimation for Extremely Large-Scale RIS-Aided Communication Systems
Lanqing Zhi, Hongwei Wang, Lingxiang Li et al. (+1 more)
This paper studies wideband channel estimation for OFDM systems assisted by extremely large RIS (XL-RIS). Due to the large aperture of XL-RISs, the user equipment may operate in the near-field region, while the base station-XL-RIS link remains in the far field, leading to a cascaded channel with hybrid near-field and far-field characteristics. Moreover, wideband effects further complicate channel estimation in mmWave/THz systems. To address these challenges, we propose a frequency-independent orthogonal dictionary by augmenting the discrete
Fourier transform (DFT)
matrix with additional parameters, which enables an efficient representation of the wideband cascaded channel using a two-dimensional block-sparse structure. Based on this property, the considered channel estimation problem is effectively solved within a tailored compressed sensing framework. Simulation results demonstrate that the proposed method significantly outperforms conventional polar-domain channel estimation approaches in terms of estimation accuracy.
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All authors Lanqing Zhi, Hongwei Wang, Lingxiang Li, Zhi Chen
Systems and Control
Full Timescale Hierarchical MPC-MTIP Framework for Hybrid Energy Storage Management in Low-Carbon Industrial Microgrid
Daniyaer Paizulamu, Lin Cheng, Ning Qi et al. (+2 more)
Uncertainties in balancing generation and load in low-carbon industrial microgrids (IMGs) make hybrid energy storage systems (HESS) crucial for their stable and economic operation. Existing model predictive control (MPC) techniques typically enforce periodic state of charge (SOC) constraints to maintain long term stability. However, these hard constraints compromise dispatch flexibility near the end of the prediction horizon, preventing sufficient energy release during critical peaks and leading to optimization infeasibility. This paper eliminates the periodic SOC constraints of individual storage units and proposes a novel full-timescale hierarchical MPC scheduling framework. Specifically, comprehensive physical and cost models are established for the HESS composed of flywheel, battery, compressed-air, and hydrogen-methanol energy storage. The control problem is decoupled into a hierarchical MPC architecture. Furthermore, a novel adaptive feedback mechanism based on micro trajectory inverse projection (MTIP) is embedded into the scheduling process, accurately mapping the high frequency dynamic buffering capabilities of lower tier storages into the upper decision space to generate dynamic boundaries. Experiments using 14 consecutive months of second-level data from a real-world IMG validate the effectiveness of the proposed method, demonstrating its significant superiority over existing approaches. By effectively preventing limit violations and deadlocks in lower-tier storages under extreme fluctuations, it achieves a 97.4% net load smoothing rate and a 62.2% comprehensive cycle efficiency.
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All authors Daniyaer Paizulamu, Lin Cheng, Ning Qi, Zhengmao Li, Nikos D. Hatziargyriou
Systems and Control · Cross-listed from cs.RO
RTD-RAX: Fast, Safe Trajectory Planning for Systems under Unknown Disturbances
Evanns Morales-Cuadrado, Long Kiu Chung, Shreyas Kousik et al. (+1 more)
Reachability-based Trajectory Design (RTD) is a provably safe, real-time trajectory planning framework that combines offline reachable-set computation with online trajectory optimization. However, standard RTD implementations suffer from two key limitations: conservatism induced by worst-case reachable-set overapproximations, and an inability to account for real-time disturbances during execution. This paper presents RTD-RAX, a runtime-assurance extension of RTD that utilizes a non-conservative RTD formulation to rapidly generate goal-directed candidate trajectories, and utilizes mixed monotone reachability for fast, disturbance-aware online safety certification. When proposed trajectories fail safety certification under real-time uncertainty, a repair procedure finds nearby safe trajectories that preserve progress toward the goal while guaranteeing safety under real-time disturbances.
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All authors Evanns Morales-Cuadrado, Long Kiu Chung, Shreyas Kousik, Samuel Coogan
Signal Processing
Extreme-MIMO Field Trials in 7 GHz Band: Unlocking the Potential of New Spectrum for 6G
Seunghyun Lee, Jungmin Yoon, Sangwon Jung et al. (+5 more)
The frequency range around 7 GHz has emerged as a promising upper mid-band spectrum for 6th generation (6G), offering a practical balance between coverage and capacity. To fully exploit this band, however, future systems require substantially stronger beamforming and spatial multiplexing capability than today's 5G 64-port commercial deployments. This article investigates extreme multiple-input multiple-output (X-
MIMO) with 256 digital ports as a practical 6G architecture for 7 GHz operation. First, through system-level simulations, we examine the throughput benefits and design trade-offs of increasing the number of base station (BS) and user equipment (UE) digital
antenna ports, including comparisons between 128-port and 256-port configurations. We then present a 256-port 7 GHz BS and UE prototype and report field-trial results obtained in urban outdoor environments. The measurements demonstrate the feasibility of 8-layer downlink single-user
MIMO over a 100 MHz bandwidth, achieving more than 3 Gbps for a single user under urban outdoor propagation conditions. Channel analysis based on measured data further suggests how the large digital aperture of X-
MIMO supports high-order spatial multiplexing even with limited dominant angular clusters. Finally, we identify key challenges and outline research directions toward practical deployment of 7 GHz X-
MIMO systems for 6G.
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All authors Seunghyun Lee, Jungmin Yoon, Sangwon Jung, Young-Han Nam, Gary Xu, Chan-Byoung Chae, Juho Lee, Jianzhong Zhang
Signal Processing · Cross-listed from cs.IT
Rateless DeepJSCC for Broadcast Channels: a Rate-Distortion-Complexity Tradeoff
Zijun Qin, Jingxuan Huang, Zesong Fei et al. (+3 more)
In recent years, numerous data-intensive broadcasting applications have emerged at the wireless edge, calling for a flexible tradeoff between distortion, transmission rate, and processing complexity. While deep learning-based joint source-channel coding (DeepJSCC) has been identified as a potential solution to data-intensive communications, most of these schemes are confined to worst-case solutions, lack adaptive complexity, and are inefficient in broadcast settings. To overcome these limitations, this paper introduces nonlinear transform rateless source-channel coding (NTRSCC), a variable-length JSCC framework for broadcast channels based on rateless codes. In particular, we integrate learned source transformations with physical-layer LT codes, develop unequal protection schemes that exploit decoder side information, and devise approximations to enable end-to-end optimization of rateless parameters. Our framework enables heterogeneous receivers to adaptively adjust their received number of rateless symbols and decoding iterations in belief propagation, thereby achieving a controllable tradeoff between distortion, rate, and decoding complexity. Simulation results demonstrate that the proposed method enhances image broadcast quality under stringent communication and processing budgets over heterogeneous edge devices.
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All authors Zijun Qin, Jingxuan Huang, Zesong Fei, Haichuan Ding, Yulin Shao, Xianhao Chen
Audio and Speech Processing
DiT-Flow: Speech Enhancement Robust to Multiple Distortions based on Flow Matching in Latent Space and Diffusion Transformers
Tianyu Cao, Helin Wang, Ari Frummer et al. (+7 more)
Recent advances in generative models, such as
diffusion and flow matching, have shown strong performance in audio tasks. However,
speech enhancement (SE) models are typically trained on limited
datasets and evaluated under narrow conditions, limiting real-world applicability. To address this, we propose DiT-Flow, a flow matching-based SE framework built on the latent
Diffusion Transformer (DiT) backbone and trained for robustness across diverse distortions, including noise, reverberation, and compression. DiT-Flow operates on compact variational auto-encoders (VAEs)-derived latent features. We validated our approach on StillSonicSet, a synthetic yet acoustically realistic
dataset composed of LibriSpeech, FSD50K, FMA, and 90 Matterport3D scenes. Experiments show that DiT-Flow consistently outperforms state-of-the-art generative SE models, demonstrating the effectiveness of flow matching in multi-condition
speech enhancement. Despite ongoing efforts to expand synthetic data realism, a persistent bottleneck in SE is the inevitable mismatch between training and deployment conditions. By integrating LoRA with the MoE framework, we achieve both parameter-efficient and high-performance training for DiT-Flow robust to multiple distortions with using 4.9% percentage of the total parameters to obtain a better performance on five unseen distortions.
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All authors Tianyu Cao, Helin Wang, Ari Frummer, Yuval Sieradzki, Adi Arbel, Laureano Moro Velazquez, Jesus Villalba, Oren Gal, Thomas Thebaud, Najim Dehak
Systems and Control · Cross-listed from cs.IT
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications
Che Chen, Lanhua Li, Shimin Gong et al. (+3 more)
In this paper, we employ multiple
UAVs to accelerate data transmissions from ground users (GUs) to a remote base station (BS) via the
UAVs' relay communications. The
UAVs' intermittent information exchanges typically result in delays in acquiring the complete system state and hinder their effective collaboration. To maximize the overall throughput, we first propose a delay-tolerant multi-
agent deep reinforcement learning (MADRL)
algorithm that integrates a delay-penalized reward to encourage information sharing among
UAVs, while jointly optimizing the
UAVs' trajectory planning, network formation, and transmission control strategies. Additionally, considering information loss due to unreliable channel conditions, we further propose a spatio-temporal
attention based prediction approach to recover the lost information and enhance each
UAV's awareness of the network state. These two designs are envisioned to enhance the network capacity in
UAV-assisted wireless networks with limited communications. The simulation results reveal that our new approach achieves over 50% reduction in information delay and 75% throughput gain compared to the conventional MADRL. Interestingly, it is shown that improving the
UAVs' information sharing will not sacrifice the network capacity. Instead, it significantly improves the learning performance and throughput simultaneously. It is also effective in reducing the need for
UAVs' information exchange and thus fostering practical deployment of MADRL in
UAV-assisted wireless networks.
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All authors Che Chen, Lanhua Li, Shimin Gong, Yu Zhao, Yuming Fang, Dusit Niyato
Signal Processing
Battery health reporting fails independent validation across manufacturers
Jeongju Park, Kyungkak Kim, Seungho Geum et al. (+3 more)
Battery state-of-health (SOH) reported by on-board battery management systems (BMS) is the primary metric available to electric vehicle (EV) owners and regulators, yet no study has validated its reliability across manufacturers against independent measurements. Here we show, through an epidemiological study of 1,114 EVs spanning five manufacturers and 375 days, that battery health reporting is fundamentally unreliable: real capacity differences of 12-25% exist within every model, but BMS SOH fails to track them, with correlations ranging from h̊o} = 0.10 (non-significant) to h̊o} = 0.62 only under restrictive filtering, while 384 vehicles do not expose SOH at all. A manufacturer-independent electrochemical marker achieves 74-89% degradation classification accuracy across all platforms without requiring BMS data, and a controlled laboratory validation on
cells identical to those in the fleet confirms that partial-voltage-window charge measurements track reference capacity with h̊o} > 0.80 across all 60 voltage windows (p < 0.001). These findings reveal a structural information asymmetry with direct implications for the EU Battery Regulation's 2027 SOH transparency mandate, California's Advanced Clean Cars (ACC) II durability requirements, warranty enforcement, used-vehicle valuation, right-to-repair legislation, and second-life battery
markets.
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All authors Jeongju Park, Kyungkak Kim, Seungho Geum, Junhyung Lee, Hyeongyu Son, Sekyung Han
Systems and Control · Cross-listed from cs.RO
Conformal Koopman for Embedded Nonlinear Control with Statistical Robustness: Theory and Real-World Validation
Koki Hirano and Hiroyasu Tsukamoto
We propose a fully data-driven, Koopman-based framework for statistically robust control of discrete-time nonlinear systems with linear
embeddings. Establishing a connection between the Koopman operator and contraction theory, it offers distribution-free probabilistic bounds on the state tracking error under Koopman modeling uncertainty. Conformal prediction is employed here to rigorously derive a bound on the state-dependent modeling uncertainty throughout the trajectory, ensuring safety and robustness without assuming a specific error prediction structure or distribution. Unlike prior approaches that merely combine conformal prediction with Koopman-based control in an open-loop setting, our method establishes a closed-loop control architecture with formal guarantees that explicitly account for both forward and inverse modeling errors. Also, by expressing the tracking error bound in terms of the control parameters and the modeling errors, our framework offers a quantitative means to formally enhance the performance of arbitrary Koopman-based control. We validate our method both in numerical simulations with the Dubins car and in real-world experiments with a highly nonlinear flapping-wing drone. The results demonstrate that our method indeed provides formal safety guarantees while maintaining accurate tracking performance under Koopman modeling uncertainty.
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Signal Processing
Digital Self-Interference Cancellation in Full-Duplex Radios: A Fundamental Limit Perspective
Limin Liao, Jun Sun, Junzhi Wang et al. (+4 more)
D-SIC is of crucial importance for the implementation of IBFD
radios. Unfortunately, the achievable performance limit remains underexplored. To fill this gap, in this paper we aim to explore the performance limit, i.e., the minimum residual self-interference (RSI) of the most commonly used PH canceller, and provide the achievable pilot design accordingly. To this end, we first conduct a systematic analysis of the RSI power for the PH canceller, which takes into account both the truncation-induced error and the noise-induced error, whereas the former is usually ignored in the existing works. To simplify the performance analysis of RSI power, we employ the generalized Laguerre
polynomial (GLP)-based PH canceller instead of the conventional monomial-based one, due to the appealing orthogonality property of the GLP for Gaussian inputs. With the GLP representation of the PH canceller, we further prove that the least-squares channel
estimator is asymptotically unbiased, thus demonstrating the asymptotic optimality of Gaussian pilot sequences. Moreover, for the pilot sequence with a finite length, a succinct criterion for minimizing the RSI, namely, the condition-number-to-minimum
eigenvalue ratio (CMER) criterion, which essentially balances the truncation-induced and noise-induced error, is presented. By contrast, the existing works normally consider the latter only. Interestingly, it is revealed that an appropriate PAPR of the pilot sequence is of critical importance to achieve the above balance. Simulation results demonstrate that the pilot sequence optimized according to our proposed CMER criterion can achieve an RSI as low as -87.3 dBm, which is over 14 dB lower than that of HE-LTF and over 6 dB lower than that of the state-of-the-art pilot sequence proposed in [1], provided that the order of the PH canceller is no higher than 9 because of the complexity constraint.
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All authors Limin Liao, Jun Sun, Junzhi Wang, Chao Deng, Jizhao Wang, Fangsen Li, Yingzhuang Liu
Systems and Control · Cross-listed from cs.RO
Auction-Based Task Allocation with Energy-Conscientious Trajectory Optimization for AMR Fleets
Jiachen Li, Soovadeep Bakshi, Jian Chu et al. (+2 more)
This paper presents a hierarchical two-stage framework for multi-
robot task allocation and trajectory optimization in asymmetric task spaces: (1) a sequential auction allocates tasks using closed-form bid functions, and (2) each
robot independently solves an optimal control problem for energy-minimal trajectories with a physics-based battery model, followed by a collision avoidance refinement step using pairwise proximity penalties. Event-triggered warm-start rescheduling with bounded trigger frequency handles
robot faults, priority arrivals, and energy deviations. Across 505 scenarios with 2-20
robots and up to 100 tasks on three factory layouts, both energy- and distance-based auction variants achieve 11.8% average energy savings over nearest-task allocation, with rescheduling latency under 10 ms. The central finding is that bid-metric performance is regime-dependent: in uniform workspaces, distance bids outperform energy bids by 3.5% (p < 0.05, Wilcoxon) because a 15.7% closed-form approximation error degrades bid ranking accuracy to 87%; however, when workspace friction heterogeneity is sufficient (r < 0.85 energy-distance correlation), a zone-aware energy bid outperforms distance bids by 2-2.4%. These results provide practitioner guidance: use distance bids in near-uniform terrain and energy-aware bids when friction variation is significant.
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All authors Jiachen Li, Soovadeep Bakshi, Jian Chu, Shihao Li, Dongmei Chen
Systems and Control
IF-CPS: Influence Functions for Cyber-Physical Systems – A Unified Framework for Diagnosis, Curation, and Safety Attribution
Jiachen Li, Shihao Li, Soovadeep Bakshi et al. (+2 more)
Neural network controllers trained via behavior cloning are increasingly deployed in cyber-physical systems (CPS), yet practitioners lack tools to trace controller failures back to training data. Existing data attribution methods assume i.i.d.\ data and standard loss targets, ignoring CPS-specific properties: closed-loop dynamics, safety constraints, and temporal trajectory structure. We propose IF-CPS, a modular influence function framework with three CPS-adapted variants: safety influence (attributing constraint violations), trajectory influence (temporal discounting over trajectories), and propagated influence (tracing effects through plant dynamics). We evaluate IF-CPS on six
benchmarks across diagnosis, curation, and safety attribution tasks. IF-CPS improves over standard influence functions in the majority of settings, achieving AUROC $1.00$ in Pendulum (5-10% poisoning), $0.92$ vs.\ $0.50$ in HVAC (10%), and the strongest constraint-boundary correlation (Spearman $\rho = 0.55$ in Pendulum).
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All authors Jiachen Li, Shihao Li, Soovadeep Bakshi, Jiamin Xu, Dongmei Chen
Systems and Control
Stochastic Trajectory Influence Functions for LQR: Joint Sensitivity Through Dynamics and Noise Covariance
Jiachen Li, Shihao Li, Soovadeep Bakshi et al. (+2 more)
We present a three-level influence hierarchy for data valuation in
stochastic LQR. At the model level, the trajectory influence surrogate $\IFm_k := H^{-1}g_k$ approximates the leave-one-trajectory parameter shift. At the control level with fixed covariance, the usual fixed-noise score is obtained by composing $\IFm_k$ with the Riccati gradient of $\tr(P(\theta)\Sigma)$. At the
stochastic control level, the plug-in cost depends additionally on the residual covariance estimate $\hat W$, so removing a trajectory perturbs the cost through both the dynamics and the covariance channels. We derive an exact leave-one-trajectory decomposition of the covariance shift into a direct-removal term and a parameter-shift term, show that the additional first-order contribution is a simple residual cross-moment, and obtain a
stochastic influence score built directly on $\IFm_k$. The resulting method preserves the amortized structure of prior work: after one Hessian factorization and one adjoint Lyapunov solve, each trajectory requires only a dot product plus an $O(n_x^2)$ direct-removal correction. The shared Hessian solves can also be performed iteratively by conjugate gradients when explicit factorization is undesirable. The new covariance remainder is explicit and does not involve Lyapunov-operator amplification; the only amplified term is the familiar Riccati remainder inherited from fixed-covariance influence analysis. Numerical results on two linear systems show that accounting for the estimated covariance substantially improves agreement with exact leave-one-trajectory retraining, especially under heterogeneous noise.
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All authors Jiachen Li, Shihao Li, Soovadeep Bakshi, Jiamin Xu, Dongmei Chen
Systems and Control
Evaluating Power Flow Manifold from Local Data around a Single Operating Point via Geodesics
Qirui Zheng, Dan Wu, Franz-Erich Wolter et al. (+1 more)
The widespread adoption of renewable energy poses a challenge in maintaining a feasible operating point in highly variable scenarios. This paper demonstrates that, within a feasible region of a power system that meets practical stability requirements, the power flow equations define a smooth bijection between nodal voltage phasors (angle and magnitude) and nodal active/reactive power injections. Based on this theoretical foundation, this paper proposes a data-based power flow evaluation method that can imply the associated power flow
manifold from a limited number of data points around a single operating point. Using techniques from differential geometry and analytic functions, we represent geodesic curves in the associated power flow
manifold as analytic functions at the initial point. Then, a special algebraic structure of the power flow problem is revealed and applied to reduce the computation of all higher-order partial derivatives to that of the first-order ones. Integrating these techniques yields the proposed data-based evaluation method, suggesting that a small number of local measurements around a single operating point is sufficient to imply the entire associated power flow
manifold. Numerical cases with arbitrary directional variations are tested, certifying the efficacy of the proposed method.
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All authors Qirui Zheng, Dan Wu, Franz-Erich Wolter, Sijia Geng
Image and Video Processing
Unregistered Spectral Image Fusion: Unmixing, Adversarial Learning, and Recoverability
Jiahui Song, Sagar Shrestha and Xiao Fu
This paper addresses the fusion of a pair of spatially unregistered hyperspectral image (HSI) and multispectral image (MSI) covering roughly overlapping regions. HSIs offer high spectral but low spatial resolution, while MSIs provide the opposite. The goal is to integrate their complementary information to enhance both HSI spatial resolution and MSI spectral resolution. While hyperspectral-multispectral fusion (HMF) has been widely studied, the unregistered setting remains challenging. Many existing methods focus solely on MSI super-resolution, leaving HSI unchanged. Supervised deep learning approaches were proposed for HSI super-resolution, but rely on accurate training data, which is often unavailable. Moreover, theoretical analyses largely address the co-registered case, leaving unregistered HMF poorly understood. In this work, an unsupervised framework is proposed to simultaneously super-resolve both MSI and HSI. The method integrates coupled spectral unmixing for MSI super-resolution with latent-space
adversarial learning for HSI super-resolution. Theoretical guarantees on the recoverability of the super-resolution MSI and HSI are established under reasonable generative models – providing, to our best knowledge, the first such insights for unregistered HMF. The approach is validated on semi-real and real HSI-MSI pairs across diverse conditions.
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Signal Processing
Rydberg Atomic Receivers for Net-Zero 6G Wireless Communication and Sensing: Progress, Experiments, and Sustainable Prospects
Yi Tao, Zhen Gao, Zhiao Zhu et al. (+6 more)
Against the backdrop of the global drive to advance the green transformation of the information and communications technology (ICT) industry and leverage technological innovation to facilitate the achievement of Net-Zero carbon goals, research into Rydberg atomic receivers (RAREs) is gaining significant interest. RAREs leverage the electron transition phenomenon for signal reception, offering significant advantages over conventional
radio frequency receivers in terms of miniaturized
antenna design, high sensitivity, robust interference resistance, and compact form factors, which positions them as a competitive alternative for meeting zero-carbon communication demands. This article systematically elaborates on the basic principle, state-of-the-art progress, and novel experiments of RAREs in
quantum wireless communication and sensing. In this first-of-its-kind work, we experimentally verify the RARE-based orthogonal frequency division multiplexing transmission and reveal the potential of deep learning design in optimizing
quantum wireless systems. Finally, we delve into the prospect of integrating RARE with existing cutting-edge application scenarios, while mapping out critical pathways for developing Rydberg-based wireless systems.
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All authors Yi Tao, Zhen Gao, Zhiao Zhu, De Mi, Zhonghuai Wu, Zijian Zhang, Fusang Zhang, Dezhi Zheng, Sheng Chen
Audio and Speech Processing · Cross-listed from cs.CL
TaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild
Kai-Wei Chang, Yi-Cheng Lin, Huang-Cheng Chou et al. (+9 more)
Speech technologies have advanced rapidly and serve diverse populations worldwide. However, many
languages remain underrepresented due to limited resources. In this paper, we introduce TaigiSpeech, a real-world
speech intent
dataset in Taiwanese Taigi (aka Taiwanese Hokkien/Southern Min), which is a low-resource and primarily spoken
language. The
dataset is collected from older adults, comprising 21 speakers with a total of 3k utterances. It is designed for practical intent detection scenarios, including healthcare and home assistant applications. To address the scarcity of labeled data, we explore two data mining strategies with two levels of supervision: keyword match data mining with LLM pseudo labeling via an intermediate
language and an audio-visual framework that leverages
multimodal cues with minimal textual supervision. This design enables scalable
dataset construction for low-resource and unwritten spoken
languages. TaigiSpeech will be released under the CC BY 4.0 license to facilitate broad adoption and research on low-resource and unwritten
languages. The project website and the
dataset can be found on https://kwchang.org/taigispeech.
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All authors Kai-Wei Chang, Yi-Cheng Lin, Huang-Cheng Chou, Wenze Ren, Yu-Han Huang, Yun-Shao Tsai, Chien-Cheng Chen, Yu Tsao, Yuan-Fu Liao, Shrikanth Narayanan, James Glass, Hung-yi Lee
Systems and Control
Emission reduction potential of freeway stop-and-go wave smoothing
Junyi Ji, Derek Gloudemans, Gergely Zachár et al. (+4 more)
The real-world potential of stop-and-go wave smoothing at scale remains largely unquantified. Smoothing freeway waves requires opening a gap large enough for them to dissipate, but that gap is often impractically large. We propose a counterfactual wave-smoothing
benchmark that reconstructs a smooth, feasible trajectory from each empirical trajectory by solving a quadratic program with fixed boundary conditions and a maximum-gap constraint, and we use the MOVES model to estimate the resulting emission reduction potential. Applying the framework to nine weeks of weekday peak-period data from the I-24 MOTION testbed, which exhibits rich day-to-day variation in wave dynamics, we find meaningful potential for passenger cars under a 0.1-mile maximum-gap constraint: average CO2 reductions of 9.80% to 14.08% across lanes, with 12.94% to 25.70% in CO, 24.29% to 29.76% in HC, and 29.40% to 36.03% in NOx. Trucks emerge as particularly attractive targets, showing roughly twice the reduction potential of passenger cars.
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All authors Junyi Ji, Derek Gloudemans, Gergely Zachár, William Barbour, Jonathan Sprinkle, Benedetto Piccoli, Daniel B. Work