Search Results (50)

Autonomous Vehicles (AVs) have been widely recognized as a promising solution to urban commuting congestion. However, quantitative evidence based on city-scale simulations of complete road networks in megacities remains limited. This study uses the complete urban road network of Beijing to investigate the influence of autonomous driving on commuting efficiency. Eleven autonomous vehicle penetration scenarios ranging from 0% to 100% at 10% intervals are established within the Simulation of Urban MObility (SUMO) microscopic traffic simulation platform. Human-driven vehicles are modeled using the Krauss car-following model, whereas autonomous vehicles are represented by the Cooperative Adaptive Cruise Control (CACC) model. The vehicle behavioral parameters are literature-based, adopted from published studies and open test data rather than calibrated against empirical Beijing traffic data, while the road network and commuting demand are constructed from Beijing-specific OpenStreetMap and mobile-signaling data. The simulation results reveal three major findings. First, autonomous driving exhibits a gradual efficiency transition over an approximate penetration range of 30% to 50% (identified qualitatively from the simulation trend rather than by a formal statistical change-point estimate). Below this threshold, behavioral heterogeneity between autonomous and human-driven vehicles intensifies traffic flow instability, whereas above it, the cooperative control capability of CACC becomes dominant and substantially improves overall network performance. Second, under full autonomous vehicle penetration, the city-wide average commuting speed increases from 7.20 m/s to 8.27 m/s, representing a 15% gain in the trip-weighted mean commuting speed (distinct from the 16% gain in the flow-weighted network speed reported in the Results), while the mean in-network simulated travel time per completed trip decreases from 561 s to 270 s. This travel-time value is an operational in-network measure and is not directly comparable to a full perceived door-to-door commute. Third, the efficiency benefits of autonomous driving display significant spatial heterogeneity. Speed improvements reach 16% to 20% on expressways and radial commuting corridors but remain between 4% and 8% on urban arterial roads. These findings indicate that the potential efficiency gains associated with autonomous driving, estimated here under fixed commuting demand and therefore as an upper bound, are constrained by the spatial characteristics of the road network. The results provide quantitative evidence supporting priority deployment of autonomous vehicles on expressways and major commuting corridors in megacities. Full article

Cooperative adaptive cruise control (CACC) platoons rely on Vehicle-to-Vehicle communication and GPS to maintain sub-second headways, creating cyberattack surfaces underrepresented in standard surrogate-safety metrics. We built a fully equation-based, Routh–Hurwitz- and Lp-string-stability-verified simulation of a ten-follower (eleven-vehicle, including the leader) CACC platoon (point-mass dynamics, actuator lag, PD spacing control) and subjected it to a two-channel GPS-spoofing attack corrupting both the attacked vehicle’s control loop and its broadcast position; velocity and acceleration broadcasts, and the CACC feed-forward term they drive, are left uncorrupted, so the reported boundaries are conditional on this restricted, single-channel threat model and should be read as a lower bound on attack severity rather than a worst case. Across a 64-cell severity–duration grid (2–20 m, 1–10 s; h = 0.6 s), minimum time-to-collision fell from 31.7 s to a simulated collision in 6/64 cells (9.4%), driven more by magnitude than duration; the disturbance decays sharply after the first hop rather than cascading down the platoon, so the resulting risk is local, not cascading. A 48-cell headway grid showed h ≥ 0.7 s eliminated all collisions at the originally tested attack duration (3/8 → 0/8 at fixed severity), a result that held under two alternative controller-gain sets tested for sensitivity and was largely, though not universally, robust to a substantially stiffer third set. A position sweep found risk invariant across nine of ten platoon positions. Batch-computed first-hop propagation and tail-to-origin amplification ratios showed the disturbance transiently amplifies (ratio > 1) at its first hop in a third of tested attacks despite decaying three orders of magnitude by the platoon’s tail, a behavior distinct from the front-injected Lp string stability verified separately. Peak root-mean-squared jerk stayed within the comfortable range (≤1 m/s³) in every tested cell, including collisions, showing collision and comfort risk are governed by different parameters. Embedding a representative detection and elastic-control layer alongside headway optimization eliminated collisions within the tested range and remained robust at three times that severity, where headway alone failed; because the detector’s residual is computed directly from the true offset magnitude and detector failure is not modeled, this joint-defense result is illustrative rather than a validated-detector-calibrated estimate. These results give a reproducible, quantified basis for headway- and detection-based mitigation policy in connected-vehicle platoons. Full article

Vehicle-to-everything (V2X)-enabled cooperative adaptive cruise control (CACC) is a key technology for improving both traffic efficiency and driving safety in vehicle-platooning scenarios. However, real-world platoons consist of heterogeneous vehicles with different actuation, computation, and mechanical delays, and communication latency also varies over time. Therefore, conventional approaches based on homogeneous vehicles and fixed-delay assumptions may fail to guarantee physical rear-end collision avoidance under severe driving conditions. This paper proposes Safe PF-CACC, a predictor-feedback-based CACC framework that integrates a V2X-aware safe inter-vehicle distance (Safe IV Distance) model with adaptive time-headway scheduling for heterogeneous vehicle platoons. The proposed Safe IV Distance is computed by considering communication latency, vehicle dynamic delays, and friction-dependent braking limits. It consists of three components: a minimum margin (MM) for low-speed and standstill conditions, a response-lag loss (RLL) induced by communication and vehicle dynamic delays, and a braking-performance limit (BPL) caused by road-friction-dependent braking capability. The resulting Safe IV Distance is converted into a dynamic effective time headway and incorporated into the predictor-feedback (PF) controller, while a filtering process is applied to suppress abrupt gain-scheduling variations. To evaluate the proposed framework, three representative CACC scenarios were considered: heterogeneous passenger-vehicle platooning, emergency vehicle platooning, and truck platooning. The simulation results show that overly short spacings without real-time delay awareness can cause collisions in high-speed and emergency driving scenarios, whereas overly conservative spacings improve safety at the cost of increased road occupancy. In the heterogeneous passenger-vehicle scenario, the proposed Safe PF-CACC reduces the maximum jerk and mean spacing by 20.6% and 49.4%, respectively, compared with the existing conservative method. In the emergency vehicle scenario, it achieved collision-free operation while reducing the maximum jerk and mean spacing by 18.6% and 53.2%, respectively. In the truck-platooning scenario, stable jerk and acceleration responses are maintained while the mean spacing is reduced by 59.6%. These results demonstrate that the proposed framework provides a practical integrated control approach for maintaining both control stability and physical safety in CACC systems under time-varying communication delays and road friction uncertainty. Full article

Cooperative adaptive cruise control (CACC), as an extension of adaptive cruise control (ACC), is an intelligent transportation approach for connected and automated vehicles. By using vehicle-to-vehicle information, CACC improves longitudinal tracking performance, traffic throughput, and string-stable platoon behavior. However, controller tuning remains sensitive to vehicle-dynamics parameters, spacing-policy selection, fractional-order dynamics, and communication delay. This paper presents an analytical parameter-space-based fractional-order PD (FOPD) controller synthesis framework for string-stable CACC systems. For the constant-time headway spacing policy, the controller parameters are investigated in the $(k_p,k_d,\mu )$ parameter space, where the fractional differentiation order $\mu$ is considered as an additional design variable. To obtain the feasible stabilizing regions, the fractional-order characteristic equation is evaluated on the imaginary axis, and the delay-dependent stability boundaries are derived through a frequency-domain boundary-locus formulation. The stabilizing gain regions are constructed through the complex-root boundary (CRB), real-root boundary (RRB), and infinite-root boundary (IRB), which provide an interpretable graphical basis for controller-gain and fractional-order selection. In addition, the effect of the headway time on the admissible stability region is examined jointly with the fractional order. The proposed structure is implemented with a feedforward controller that uses the acceleration information of the preceding vehicle under a predecessor-vehicle-following communication topology. The selected fractional-order CACC (FO-CACC) controller is validated in an eight-vehicle platoon simulation environment and compared with integer-order ACC (IO-ACC), fractional-order ACC (FO-ACC), and integer-order CACC (IO-CACC) configurations. The results show that the proposed parameter-space approach enables systematic FOPD tuning and that the selected FO-CACC controller satisfies the frequency-domain string-stability requirement while maintaining smooth time-domain responses in position, velocity, acceleration, headway time, spacing error, and control input. Additional simulations under the New European Driving Cycle (NEDC) and the FTP-75 (Federal Test Procedure 1975) driving cycles further indicate that the proposed FO-CACC structure maintains accurate spacing regulation and bounded acceleration behavior under standard drive-cycle conditions. Overall, the results indicate that the fractional-order parameter provides an effective design freedom for improving string-stable cooperative platoon performance. Full article

Secure vehicle-to-vehicle communication is essential for electric-vehicle platoons because broadcast wireless links may expose safety and control messages to passive eavesdropping. This paper investigates a physical-layer security (PLS) framework for electric-vehicle (EV) platoons under Rician fading, representing the line-of-sight conditions common in highway platooning. The proposed Jamming-Aided Cooperative Relay Selection (JACRS) framework uses an amplify-and-forward relay, destination-assisted full-duplex friendly jamming, residual self-interference modelling, and a strict total transmit power budget. Relay selection is evaluated using a full-channel state information (CSI) secrecy-selection benchmark, a practical legitimate-link CSI rule, and a deterministic platoon-geometry-aware rule based on Cooperative Adaptive Cruise Control (CACC) position information without instantaneous eavesdropper CSI. Monte Carlo simulations, supported by semi-analytical secrecy-outage and deterministic-slot benchmarks, compare the proposed scheme with Rayleigh and no-jamming amplify-and-forward (AF) baselines. Under the simulated geometry, the scheme achieves a peak ergodic secrecy rate close to 5.0 bps/Hz at 40 dBm and reduces interception risk by 78.07% relative to the Rayleigh baseline. Relay diversity reduces secrecy outage from 14.14% to 0.04% under full CSI and to 0.22% using legitimate-link CSI. The geometry-aware rule reduces the gap between practical legitimate-link selection and the full-CSI benchmark, indicating that platoon position information can improve relay selection under the tested conditions. Full article

This paper proposes a calibration-oriented framework for cooperative adaptive cruise control based on a linear quadratic regulator formulation. A simulation-based architecture is developed by integrating the controller with a nonlinear longitudinal platoon model that explicitly accounts for actuator saturation and tyre–road friction limits, enabling the analysis of platoon behaviour under realistic operating conditions. A systematic offline calibration methodology is introduced based on multidimensional performance maps, relating key performance indicators associated with collision avoidance, comfort, and energy efficiency to controller and spacing-policy tuning parameters. The map-based approach enables a structured exploration of competing objectives and provides a quantitative assessment of controller sensitivity. The results show that the proposed framework can identify calibration regions that preserve collision-free operation in safety-critical manoeuvres while maintaining satisfactory tracking and comfort-related performance. In addition, the off-nominal model parameters analysis confirms that the proposed calibration approach remains effective under heterogeneous operating conditions, including vehicle parametric variation of mass, rolling resistance coefficient and drag. Overall, the results support the use of the proposed methodology as a practical tool for robust and performance-oriented controller calibration. Full article

Road traffic significantly contributes to fuel consumption and emissions. Fortunately, the advent of cooperative adaptive cruise control (CACC), facilitated by vehicle-to-vehicle (V2V) communication, reduces energy consumption and improves efficiency in transportation systems. Nevertheless, V2V communication performance (V2VCP) is highly vulnerable to degradation due to various factors. Limited comprehension exists regarding the generalized modeling of subpar V2V communication performance (SV2VCP), coupled with limited exploration of its resulting impacts on environmental sustainability. To bridge these gaps, this study presents the first attempt to assess the impact of SV2VCP on fuel consumption and exhaust emissions within the CACC framework. More specifically, we adopt the multi-predecessor following (MPF) topology and model SV2VCP scenarios, along with proposing five vehicle state update methods (VSUMs). Subsequently, by simulating various SV2VCP and driving scenarios, we comprehensively understand the effects of different VSUMs, SV2VCP, and abnormal vehicle positions on the safety, emissions, and energy consumption of the platoon. The results reveal that SV2VCP substantially impacts the fuel efficiency and emission performance of the CACC platoon, with fuel consumption during deceleration exceeding that of acceleration by approximately 14% when all vehicles are subject to SV2VCP. Furthermore, our study provides critical recommendations for optimal strategy selection, aiming to foster energy conservation and emission reductions, thereby promoting sustainable transport systems. Full article

The rapid increase in global vehicle usage has intensified challenges such as traffic congestion, frequent accidents, and energy consumption, highlighting the need for safe and efficient platooning strategies. Conventional adaptive cruise control (ACC), while widely adopted, suffers from string instability that amplifies disturbances along a platoon. Communication-based cooperative ACC (CACC) can theoretically guarantee string stability at short headways, but its dependence on costly and unreliable vehicle-to-vehicle (V2V) links limits large-scale deployment. Radar-only CACC using single-model Kalman Filter (KF) alleviates this dependency, yet its estimation accuracy degrades under abrupt maneuvers due to model mismatch. To overcome these limitations, this paper proposes a Multi-Q Interacting Multiple Model Kalman Filter (Multi-Q IMM-KF) approach that adaptively blends multiple motion models to ensure robust acceleration estimation across diverse driving conditions. A four-vehicle platoon simulation in CarSim–Simulink demonstrates that the Multi-Q IMM-KF CACC significantly reduces spacing error propagation and improves velocity tracking compared with ACC and Nominal KF-CACC, offering a cost-effective and communication-resilient solution for practical platoon control. Full article

Cooperative Intelligent Transport Systems (C-ITS) have emerged as a key enabler of more efficient, safer, and environmentally sustainable road traffic by allowing vehicles and infrastructure to exchange information and coordinate behavior. To evaluate their benefits, impact assessment studies are essential. However, most existing studies focus on individual C-ITS services in isolation, overlooking how combined deployments influence outcomes. This study addresses this gap by presenting the first systematic evaluation of individual and joint deployments of Cooperative Adaptive Cruise Control (CACC) and Green Light Optimal Speed Advisory (GLOSA) under diverse conditions. A dual-model simulation framework is applied, combining controlled artificial networks with calibrated real-world corridors in Upper Austria. This allows both statistical testing and validation of plausibility in real-world contexts. Key performance indicators include travel time and CO₂ emissions, evaluated across varying lane configurations, numbers of traffic lights, demand levels, and equipment rates. The results demonstrate that C-ITS effectiveness is strongly context-dependent: while CACC generally provides larger efficiency gains, GLOSA yields consistent emission reductions, and the combined deployment offers conditional synergies but may also diminish benefits at high demand. The study contributes a guideline for selecting service configurations based on site conditions, thereby providing practical recommendations for future C-ITS rollouts. Full article

In this paper, we propose systematic controller design guidelines to ensure both individual vehicle stability and string stability in cooperative adaptive cruise control (CACC)-based platoon systems, assuming a homogeneous platoon where all vehicles share identical dynamic models. We rigorously demonstrate that the limitation of conventional adaptive cruise control (ACC) in maintaining the target inter-vehicle distance can be effectively overcome by incorporating the desired acceleration of the preceding vehicle as a static feedforward input. Furthermore, by formulating transfer functions in the frequency domain, we analytically derive the conditions required to ensure both individual vehicle stability and string stability of the CACC system. Building on this insight, we propose a practical and theoretically well-founded design guideline for determining the proportional, derivative, and feedforward gains of control input under a constant time gap spacing policy. The proposed guidelines are validated through simulations conducted in a realistic platooning scenario involving multiple vehicles. Full article

Collision avoidance between vehicles is a great challenge, especially in the context of mixed driving of connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs). Advances in automation and connectivity technologies provide opportunities for CAVs to drive cooperatively. This paper proposes a proactive collision avoidance model, aiming to avoid collisions by controlling the speed and lane-changing behavior of CAVs. In the model, the subject vehicle first collects information about surrounding lanes and judges the traffic conditions; it then chooses to decelerate or change lanes to avoid collisions. The subject vehicle also searches for the optimal vehicle in the surrounding lanes for cooperation. The effectiveness of the proposed collision avoidance model is verified through the Python-SUMO platform. The experimental results show that the performance of the collision avoidance model is better than that of the cooperative adaptive cruise control (CACC) model in terms of average speed, lost time and the number of vehicle conflicts, proving the advantages of the proposed model in safety and efficiency. Full article

This study presents a TD3-driven Bilateral Control Model (TD3-BCM) aimed at improving the stability of mixed traffic flows in autonomous vehicle (AV) chains. By integrating deep reinforcement learning, TD3-BCM optimizes control strategies to reduce traffic oscillations, smooth speed and acceleration fluctuations, and enhance overall system performance. Stability analysis shows that TD3-BCM effectively suppresses traffic fluctuations, with system stability improving from 1.132 to 1.182 as AV penetration increases. At an AV penetration rate of 40%, TD3-BCM surpasses both Cooperative Adaptive Cruise Control (CACC) and traditional Bilateral Control Model (BCM) approaches in terms of traffic efficiency, safety, and energy use—raising trailing vehicle speed by 12.6%, shortening average headway by 19.0%, increasing Time-to-Collision (TTC) by 87.3%, and lowering fuel consumption by 14.8%. When AV penetration reaches 70%, fuel savings rise to 19.7%, accompanied by further improvements in both traffic stability and safety. TD3-BCM provides a scalable and sustainable solution for intelligent transportation systems, particularly in high-penetration AV environments, by significantly enhancing stability, operational efficiency, and road safety. Full article

In this paper, we study the stability of the stochastically heterogeneous traffic flow involving connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Taking the stochasticity of vehicle arrivals and behaviors into account, a general robust $H_{\infty }$ platoon controller is proposed to address the communication delay and unexpected disturbances such as prediction or perception errors on HDV motions. To simplify the problem complexity from a stochastically heterogeneous traffic flow to multiple long vehicle control problems, three types of sub-platoons are identified according to the CAV arrivals, and each sub-platoon can be treated as a long vehicle. The car-following behaviors of HDVs and CAVs are simulated using the optimal velocity model (OVM) and the cooperative adaptive cruise control (CACC) system, respectively. Later, the robust $H_{\infty }$ platoon controller is designed for a pair of a CAV long vehicle and an HDV long vehicle. The time-lagged system and the closed-loop system are formulated and the $H_{\infty }$ state feedback controller is designed. The robust stability and string stability of the heterogeneous platoon system are analyzed using the $H_{\infty }$ norm of the closed-loop transfer function and the time-lagged bounded real lemma, respectively. Simulation experiments are conducted considering various settings of platoon sizes, communication delays, disturbances, and CAV penetration rates. The results show that the proposed $H_{\infty }$ controller is robust and effective in stabilizing disturbances in the stochastically heterogeneous traffic flow and is scalable to arbitrary sub-platoons in various CAV penetration rates in the heterogeneous traffic flow of road vehicles. The advantages of the proposed method in stabilizing heterogeneous traffic flow are verified in comparison with a typical car-following model and the linear quadratic regulator. Full article

Predicting Signal Phase and Timing (SPaT) information and confidence levels is needed to enhance Green Light Optimal Speed Advisory (GLOSA) and/or Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems. This study proposes an architecture based on transformer encoders to improve prediction performance. This architecture is combined with different deep learning methods, including Multilayer Perceptrons (MLP), Long-Short-Term Memory neural networks (LSTM), and Convolutional Long-Short-Term Memory neural networks (CNNLSTM) to form an ensemble of predictors. The ensemble is used to make data-driven predictions of SPaT information obtained from traffic signal controllers for six different intersections along the Gallows Road corridor in Virginia. The study outlines three primary tasks. Task one is predicting whether a phase would change within 20 s. Task two is predicting the exact change time within 20 s. Task three is assigning a confidence level to that prediction. The experiments show that the proposed transformer-based architecture outperforms all the previously used deep learning methods for the first two prediction tasks. Specifically, for the first task, the transformer encoder model provides an average accuracy of 96%. For task two, the transformer encoder models provided an average mean absolute error (MAE) of 1.49 s, compared to 1.63 s for other models. Consensus between models is shown to be a good leading indicator of confidence in ensemble predictions. The ensemble predictions with the highest level of consensus are within one second of the true value for 90.2% of the time as opposed to those with the lowest confidence level, which are within one second for only 68.4% of the time. Full article

The combination of connectivity and automation allows connected and autonomous vehicles (CAVs) to operate autonomously using advanced on-board sensors while communicating with each other via vehicle-to-vehicle (V2V) technology to enhance safety, efficiency, and mobility. One of the most promising features of CAVs is cooperative adaptive cruise control (CACC). This system extends the capabilities of conventional adaptive cruise control (ACC) by facilitating the exchange of critical parameters among vehicles to enhance safety, traffic flow, and efficiency. However, increased connectivity introduces new vulnerabilities, making CACC susceptible to cyber-attacks, including false data injection (FDI) attacks, which can compromise vehicle safety. To address this challenge, we propose a secure observer-based control design leveraging Lyapunov stability analysis, which is capable of mitigating the adverse impact of FDI attacks and ensuring system safety. This approach uniquely addresses system security without relying on a known lead vehicle model. The developed approach is validated through simulation results, demonstrating its effectiveness. Full article