Real Traffic Scenarios Research Articles

Lightweight Vehicle Detection Based on Mamba_ViT.

Vehicle detection algorithms are essential for intelligent traffic management and autonomous driving systems. Current vehicle detection algorithms largely rely on deep learning techniques, enabling the automatic extraction of vehicle image features through convolutional neural networks (CNNs). However, in real traffic scenarios, relying only on a single feature extraction unit makes it difficult to fully understand the vehicle information in the traffic scenario, thus affecting the vehicle detection effect. To address this issue, we propose a lightweight vehicle detection algorithm based on Mamba_ViT. First, we introduce a new feature extraction architecture (Mamba_ViT) that separates shallow and deep features and processes them independently to obtain a more complete contextual representation, ensuring comprehensive and accurate feature extraction. Additionally, a multi-scale feature fusion mechanism is employed to enhance the integration of shallow and deep features, leading to the development of a vehicle detection algorithm named Mamba_ViT_YOLO. The experimental results on the UA-DETRAC dataset show that our proposed algorithm improves mAP@50 by 3.2% compared to the latest YOLOv8 algorithm, while using only 60% of the model parameters.

A following model considering multiple vehicles from the driver's front and rear perspectives

In the current traffic environment dominated by manual driving, existing models of drivers' rear-view perceptions are inadequate. Existing car-following models that incorporate rear-view information are primarily focused on the Internet of Vehicles (IoV) and automated driving environments. However, they fail to realistically reflect the visual processing mechanisms of human drivers, limiting their effectiveness in realistic traffic scenarios. Therefore, we propose a new following model, the multi-vehicle influence from front and rear perspectives (MVFR), that considers the influence of multiple vehicles. The MVFR model combines information from both front and rear vehicles, integrating views from the front, side front and rear. It provides an in-depth analysis of the effects of relative state differences between a vehicle and its surrounding vehicles on speed, including the effects of perspectives in both the lateral and longitudinal directions. Linear stability analysis and numerical simulation demonstrate that considering the perspectives of rear-following vehicles and lateral offset angles can improve traffic flow stability to a certain extent. Furthermore, properly considering the lateral offset distance and the number of vehicles ahead also positively affects traffic flow stability. This study reveals that observing following vehicles and considering information from multiple front vehicles enhances system stability, especially when there is no or minimal lateral offset. In contrast, focusing on fewer front vehicles is more effective for traffic flow stability when there is a large lateral offset. Experimental results using the CKQ4up dataset show that the MVFR model achieves higher accuracy than the conventional FVD model, the front-view-only improved FVD model (MFVD-RV), and the MFRHVAD-RV and MFRHVAD-AV models. Compared with models relying solely on front-view or non-visual perception, the MVFR model demonstrates a better fit, validating the advantages of this full-view perception model in manual driving environments. This innovation addresses the shortcomings of existing research, thereby enhancing the reliability of models under manual driving conditions on highways.

A Robust TrafficSignNet Algorithm for Enhanced Traffic Sign Recognition in Autonomous Vehicles Under Varying Light Conditions

Recent years have witnessed significant advancements in machine perception, particularly in the context of self-driving vehicles. The accurate detection and interpretation of road signs by these vehicles are crucial for enhancing safety, intelligence, and efficiency on the roads. Consequently, there is a growing body of research dedicated to improving traffic sign recognition technologies, a key component of intelligent transportation systems. Annual statistics highlight numerous accidents attributable to factors such as excessive speed, variable lighting conditions, and the misinterpretation of traffic signs. In response to these challenges, a novel approach for the rapid and reliable recognition of traffic signs by moving vehicles has been developed. This approach leverages a custom dataset encompassing twelve object categories and seven subcategories, reflective of road sign diversities encountered in India. A specialized algorithm, TrafficSignNet, was devised to specifically identify signs related to speed, turning, zones, and bumps. This algorithm was trained on a comprehensive dataset comprising 4,962 images, with its performance evaluated using 705 images from real traffic scenarios. The evaluation demonstrates that the model achieves remarkable accuracy across various lighting conditions, processing up to 12 frames per second. This processing rate is compatible with the high-definition standards of contemporary vehicle cameras, which is 1280 × 720 pixels. The model's effectiveness is quantified through accuracy, precision, recall, and F1 score, with respective values of 0.985, 0.978, 0.964, and 0.971, showcasing its potential to significantly contribute to the advancement of smart transportation systems.

Digital traffic state analysis for urban regions considering complex multi-directional flow changes

In this research, a digital analytical model based on three-flow full-cycle state macroscopic fundamental diagram (MFD) with observable different perimeter control management and flow loading modes has been proposed on the basis of real network and peak hour traffic flow loading scenarios in Rostov-on-Don. It is known that the aim of digital management of urban intelligent traffic is to ultimately optimize the overall state of multi-area traffic operation in the city. The goal of this research was to provide a strong basis to establish digital management strategies for intelligent transport, determine the change trends of steady-state equilibrium of regional traffic flow and their effects on full-cycle states of traffic flow under various peripheral control management strategies and traffic loading patterns. Simulation results confirmed that with the evolution of traffic state, stricter peripheral control management always results in a wider attraction area ultimately giving rise to steady-state equilibrium of traffic flow.

Scenario Identification and Classification to Support the Assessment of Advanced Driver Assistance Systems

In recent years, driver assistance systems in cars, buses, and trucks have become more common and powerful. In particular, the introduction of AI methods to sensors, signal fusion, and traffic recognition allows us to step forward from actual level-2 assistance to level-3 Advanced Driver Assistance Systems (ADAS), where driving becomes autonomous and responsibility shifts from the driver to the automobile manufacturers. This, however, requires a high-precision risk assessment of failure, which can only be achieved by extensive data acquisition and statistical analysis of real traffic scenarios (which is impossible to perform by humans). Therefore, critical driving situations have to be identified and classified automatically. This paper develops and compares two different strategies—a traditional rule-based approach derived from deterministic causal considerations, and an AI-based approach trained with idealized cut-in, cut-out, and cut-through maneuvers. Application to a 10-h measurement sequence on a German highway demonstrates that the latter has the higher performance, whereas the former misses some of the safety-relevant events to be identified.

On the Detection of Vulnerable Road Users—Simulating Limits of an FMCW Radar

The complexity of urban traffic environments poses unique challenges for automotive radar sensors. Limited range and Doppler resolution hinder the ability to distinguish closely spaced objects and detect vulnerable road users, particularly pedestrians. Therefore, it is important to know the limits of FMCW radars. To assess these limitations, the WinProp channel simulator is used to model and simulate traffic scenarios regarding the detection of the range, velocity, and SNR of targets. To validate WinProp as a simulation tool for traffic scenarios, a real traffic scenario is configured and the simulation results compared to the measurement results in order to demonstrate the suitability of WinProp for simulating traffic scenarios and evaluating radar detection performance. The close agreement between the simulation and measurement results provides validation for WinProp’s accuracy in modeling detections and signal processing within traffic scenarios. To showcase the limitations of FMCW radars, two scenarios with challenging detection conditions are simulated: an occluded pedestrian between parked vehicles and a densely populated road. A monostatic setup with a single patch antenna and a 16 × 16 patch antenna array is evaluated, using chirp bandwidths of 1 GHz and 4 GHz. The simulations have shown that although it is feasible to detect an occluded pedestrian by multipath propagation, there is still a need in improving the detection performance when there is no multipath from an occluded pedestrian. Furthermore, the results indicate that higher bandwidth improves target separation but is insufficient for occluded pedestrian detection without multipath. However, with some targets, even the range resolution at 4 GHz was not sufficient, which required a separation in the Doppler dimension, emphasizing the need for overall resolution improvement of FMCW radars. Future work should focus on developing more complex road user models and simulating a broader range of scenarios to comprehensively evaluate radar performance in road traffic environments.

A multi-stage spatial queueing model with logistic arrivals and departures consistent with the microscopic fundamental diagram and hysteresis

This paper introduces a spatial queueing model for a single bottleneck during morning peak hours. Utilizing the logistic function and after appropriate calibration, it articulates the arrival and departure flows in continuous, differentiable terms. By validating the model across different peak periods and locations, the demand model’s robustness is superior to other commonly used functions. This model also incorporates constant or varying capacity scenarios. It effectively captures key aspects of morning peak traffic, including the emergence of hysteresis loops in fundamental diagrams (FDs) of density and flow. The model’s multi-stage approach recognizes three distinct phases in traffic flow: freeflow, transition, and queued segments, ensuring spatial consistency in flow and density across these stages. It accounts for the growth of the queued segment and vehicle spillback under various bottleneck intensities, with the resulting FDs for speed and density also displaying hysteresis loops. The calibration of model parameters utilizes time-series data of traffic flow and density space–time maps derived from real-world data. The validation results accurately reflect real traffic scenarios, emulating the counterclockwise hysteresis loops observed in density and its heterogeneity, and provide both planar and three-dimensional FDs at different points along the traffic link, each mirroring real-life traffic patterns. Additionally, a comparison with the cell transmission model (CTM) reveals that the proposed model exhibits superior generalization and robustness.

Multi-Task Visual Perception for Object Detection and Semantic Segmentation in Intelligent Driving

With the rapid development of intelligent driving vehicles, multi-task visual perception based on deep learning emerges as a key technological pathway toward safe vehicle navigation in real traffic scenarios. However, due to the high-precision and high-efficiency requirements of intelligent driving vehicles in practical driving environments, multi-task visual perception remains a challenging task. Existing methods typically adopt effective multi-task learning networks to concurrently handle multiple tasks. Despite the fact that they obtain remarkable achievements, better performance can be achieved through tackling existing problems like underutilized high-resolution features and underexploited non-local contextual dependencies. In this work, we propose YOLOPv3, an efficient anchor-based multi-task visual perception network capable of handling traffic object detection, drivable area segmentation, and lane detection simultaneously. Compared to prior works, we make essential improvements. On the one hand, we propose architecture enhancements that can utilize multi-scale high-resolution features and non-local contextual dependencies for improving network performance. On the other hand, we propose optimization improvements aiming at enhancing network training, enabling our YOLOPv3 to achieve optimal performance via straightforward end-to-end training. The experimental results on the BDD100K dataset demonstrate that YOLOPv3 sets a new state of the art (SOTA): 96.9% recall and 84.3% mAP50 in traffic object detection, 93.2% mIoU in drivable area segmentation, and 88.3% accuracy and 28.0% IoU in lane detection. In addition, YOLOPv3 maintains competitive inference speed against the lightweight YOLOP. Thus, YOLOPv3 stands as a robust solution for handling multi-task visual perception problems. The code and trained models have been released on GitHub.

Efficiency of energy-generating tiles with different types of multipliers

Problem. With the urgent need for sustainable and renewable energy solutions, the integration of energy-harvesting technologies in urban infrastructure has become a focal point of innovation. Energy-generating tiles, transforming pedestrian movement into electricity, offer a viable path forward. Yet, their efficiency hinges on the internal design, particularly the type of multipliers used. Goal. This study aims to assess and compare the efficiency of energy-generating tiles equipped with helical and straight-cut gear multipliers, to identify the optimal design for maximizing energy conversion in urban environments. Methodology. An experimental setup was created to test prototypes of the energy-generating tiles under controlled foot traffic conditions. The study measured and analyzed the electrical output and efficiency of tiles with both types of gear multipliers, simulating real pedestrian traffic scenarios. Results. Energy-generating tiles featuring straight-cut gears outperformed those with helical gears in energy output and conversion efficiency. The research also illuminated the influence of pedestrian dynamics, like step force and frequency, on tile performance. Originality. This research advances renewable energy technology by offering a comprehensive analysis of energy-generating tile efficiency with different gear multipliers. It introduces an innovative method for evaluating tile performance, underscoring the significance of gear design in energy conversion. Practical value. The findings have considerable implications for sustainable urban development, suggesting that implementing energy-generating tiles in areas with high foot traffic could significantly enhance urban renewable energy sources. This study not only guides the design of more efficient energy-harvesting tiles but also encourages their broader adoption, supporting urban sustainability goals and the reduction of carbon emissions.

Service-aware real-time slicing for virtualized beyond 5G networks

Edge Intelligence is expected to play a vital role in the evolution of 5G networks, empowering them with the capability to make real-time decisions regarding various allocations related to their management and service provisioning to end-users. This shift facilitates the transition from a network-aware approach, where applications are developed to manage network quality fluctuations, to a service-aware network that self-adjusts based on the hosted applications. In this paper, we design and implement a service-aware network managed from the network edge. We utilize and assess various Machine Learning models to classify cellular network traffic flows in the backhaul, aiming to predict their future impact on network load. Leveraging these predictions, the network can proactively and autonomously reallocate slices in the Radio Access Network via programmable APIs, ensuring the demands of the traffic-generating applications are met. The approach integrates innovative MLOps methodologies for distributed and online training, enabling continuous model refinement and adaptation to evolving network dynamics. Our framework was tested in a real-world environment with realistic traffic scenarios, and the results were evaluated in real-time, down to a granularity of 10ms. Our findings indicate that the network can swiftly adjust to traffic, providing users with slices tailored to their application needs. Notably, our experiments show that under the studied settings, the users experienced up to 4 times lower latency (jitter) and nearly 4 times higher throughput when interacting with various applications, compared to the standard non-AI/ML unit. Furthermore, our dynamic scheme significantly optimizes resource allocation, ensuring energy efficiency by avoiding over- and under-provisioning of resources.

Use-case study for a smaller German community and beyond, with realistic traffic scenario and three alternative vertiport locations

Future individual air traffic will be based on air vehicles, such as drones and air-taxis. While drones, e.g., for delivery of goods may be processed decentralized, passenger air-taxis will be operated at vertiports in city centers, e.g., connected to big rail hubs, close to civil airports, but also in smaller communities in the vicinity of larger cities. Here, air-taxi noise—mainly take-off and approach—can impact residential areas inside the community but also in its neighborhood. In cooperation with a midsized community near to Munich/Germany, three potential vertiport locations in the community’s area are investigated under the aspect of noise and potentially related noise annoyance. To create a use-case with results, transferable to similar situations, realistic numbers for daily air-taxi movements between the use-case vertiport and five neighbor cities are included. These connections imply two different types of air-taxis: one tilt-wing eVTOL for the ranges up to 75 miles, and one multicopter aircraft serving on distances up to 30 miles. The UAM noise use-case study is performed for all three vertiport positions, optimized flight paths, and different flight profiles. The results regarding community noise, but also for en-route noise between the neighboring vertiports, are discussed in this paper.

Ecological Driving Framework of Hybrid Electric Vehicle Based on Heterogeneous Multi-Agent Deep Reinforcement Learning

Hybrid Electric Vehicles have great potential to be discovered in terms of energy saving and emission reduction, and ecological driving provides theoretical guidance for giving full play to their advantages in real traffic scenarios. In order to implement ecological driving strategy with the lowest cost throughout life cycle in car-following scenario, the safety and comfort, fuel economy and battery health need to be considered, which is a complex nonlinear and multi-objective coupled optimization task. Therefore, a novel multi-agent deep deterministic policy gradient (MADDPG) based framework with two heterogeneous agents to optimize adaptive cruise control and energy management strategy respectively is proposed, thereby decoupling optimization objectives of different domains. Due to the asynchronous of multi agents, different learning rate schedules are analyzed to coordinate learning process to optimize training results. And an improvement on Prioritized Experience Replay technique is proposed, which improves optimization performance of original MADDPG method by more than 10%. Simulations under mixed driving cycles show that, on the premise of ensuring car-following performance, the overall driving cost including fuel consumption and battery health degradation of MADDPG-based method can reach 93.88% of that of DP. And the proposed algorithm has good adaptability to different driving conditions.

On the Use of Biofuels for Cleaner Cities: Assessing Vehicular Pollution through Digital Twins and Machine Learning Algorithms

The air pollution caused by greenhouse gas emissions, particularly carbon dioxide (CO2), is a significant environmental concern that impacts air quality and contributes to global warming. The transportation sector plays a pivotal role in this issue, being a major contributor to CO2 emissions. In light of this situation, this article proposes a methodology that utilizes a supervised learning algorithm to estimate CO2 emissions and compare vehicles fueled with ethanol and gasoline. Additionally, the solution adopts an online, unsupervised machine learning algorithm to identify data outliers and improve the confidence in the results. Furthermore, this work incorporates the concept of digital twins, using virtual models of vehicles to carry out more extensive pollution simulations and allowing the simulation of various types of vehicles and the modeling of realistic traffic scenarios. A supervised machine learning approach was adopted to infer emission data in the model, allowing more comprehensive and meaningful comparisons between real-world and simulated measurements. The performed analyses of pollution emissions for different speeds and sections of routes demonstrate that CO2 emissions from ethanol were significantly lower than those from gasoline, favoring more sustainable fuels even in combustion engine vehicles. Adopting cleaner fuels is perceived as crucial to mitigate the negative effects of climate change, with plant-based fuels like ethanol being crucial during the transition from fossil fuels to a more sustainable vehicular landscape.

A Dual Evidential Top-View Representation to Model the Semantic Environment of Automated Vehicles

We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDAR and cameras. Our grid-based evidential model contains semantic estimates for cell occupancy and ground separately. We specify the estimation steps for input data represented by point sets, but mainly focus on input data represented by images such as disparity maps or LiDAR range images. Instead of relying on an external ground segmentation only, we deduce occupancy evidence by analyzing the surface orientation around measurements. We conduct experiments and evaluate the presented method using LiDAR and stereo camera data recorded in real traffic scenarios. Our method estimates cell occupancy robustly and with a high level of detail while maximizing efficiency and minimizing the dependency to external processing modules.

Multi-objective pedestrian tracking method based on YOLOv8 and improved DeepSORT.

A multi-objective pedestrian tracking method based on you only look once-v8 (YOLOv8) and the improved simple online and real time tracking with a deep association metric (DeepSORT) was proposed with the purpose of coping with the issues of local occlusion and ID dynamic transformation that frequently arise when tracking target pedestrians in real complex traffic scenarios. To begin with, in order to enhance the feature extraction network's capacity to learn target feature information in busy traffic situations, the detector implemented the YOLOv8 method with a high level of small-scale feature expression. In addition, the omni-scale network (OSNet) feature extraction network was then put on top of DeepSORT in order to accomplish real-time synchronized target tracking. This increases the effectiveness of picture edge recognition by dynamically fusing the collected feature information at various scales. Furthermore, a new adaptive forgetting smoothing Kalman filtering algorithm (FSA) was created to adapt to the nonlinear condition of the pedestrian trajectory in the traffic scene in order to address the issue of poor prediction attributed to the linear state equation of Kalman filtering once more. Afterward, the original intersection over union (IOU) association matching algorithm of DeepSORT was replaced by the complete-intersection over union (CIOU) association matching algorithm to fundamentally reduce the target pedestrians' omission and misdetection situation and to improve the accuracy of data matching. Eventually, the generalized trajectory feature extractor model (GFModel) was developed to tightly merge the local and global information through the average pooling operation in order to get precise tracking results and further decrease the impact of numerous disturbances on target tracking. The fusion algorithm of YOLOv8 and improved DeepSORT method based on OSNet, FSA and GFModel was named YOFGD. According to the experimental findings, YOFGD's ultimate accuracy can reach 77.9% and its speed can reach 55.8 frames per second (FPS), which is more than enough to fulfill the demands of real-world scenarios.

Moral judgment in realistic traffic scenarios: moving beyond the trolley paradigm for ethics of autonomous vehicles

AbstractThe imminent deployment of autonomous vehicles requires algorithms capable of making moral decisions in relevant traffic situations. Some scholars in the ethics of autonomous vehicles hope to align such intelligent systems with human moral judgment. For this purpose, studies like the Moral Machine Experiment have collected data about human decision-making in trolley-like traffic dilemmas. This paper first argues that the trolley dilemma is an inadequate experimental paradigm for investigating traffic moral judgments because it does not include agents’ character-based considerations and is incapable of facilitating the investigation of low-stakes mundane traffic scenarios. In light of the limitations of the trolley paradigm, this paper presents an alternative experimental framework that addresses these issues. The proposed solution combines the creation of mundane traffic moral scenarios using virtual reality and the Agent-Deed-Consequences (ADC) model of moral judgment as a moral-psychological framework. This paradigm shift potentially increases the ecological validity of future studies by providing more realism and incorporating character considerations into traffic actions.

Dual-Pulse Repeated Frequency Waveform Design of Time-Division Integrated Sensing and Communication Based on a 5G New Radio Communication System

With the development of 5G communication systems, it is a hot topic to embed integrated sensing and communication (ISAC) based on the existing 5G base station by sharing the hardware and the same frequency spectrum. In this paper, we propose a dual pulse repeated frequency (dual-PRF) waveform design of time-division ISAC (TD-ISAC) based on a 5G new radio (NR) communication system using downlink communication slots. We choose time-division mode to design waveform to avoid the interference between sensing and communication. Embedding sensing functions in a 5G NR system, we design a dual-PRF sensing slot to satisfy the constraints of common channel and uplink communication. Considering two uplink modes, namely flexible and fixed, we design two dual-PRF waveforms and illustrate the sensing theory performance of the designed waveform by the ambiguity function. Then, we exploit the designed waveform to the vehicle parameter estimation. To verify that the designed waveform has good adaptability to different signal processing methods, we realize the parameter estimation by two types of methods: the discrete Fourier transformation-based method and the compressed sensing-based method. At last, we verify the effectiveness of the designed waveform system by simulation experiments and real traffic scenarios.

Optimizing VANET Communication through Realistic Traffic Simulation

This article explores the optimization of communication in Vehicular Ad-Hoc Networks (VANETs) through the use of realistic traffic simulation in Raipur City, located in the state of Chhattisgarh. VANETs are a specialized type of Mobile Ad-Hoc Networks (MANETs) that enable vehicles to communicate with each other and with roadside infrastructure, improving road safety, traffic efficiency, and providing additional services. However, VANETs encounter challenges due to the dynamic nature of vehicular environments, including high mobility, intermittent connectivity, and varying traffic conditions. To address these challenges, this study proposes the Integration to Message Exchange (IME) method, which incorporates realistic traffic simulation into VANET communication optimization strategies. Realistic traffic simulation models vehicle behavior and interactions with the road network, considering factors such as traffic flow, congestion, and road conditions. By integrating this simulation into VANET communication optimization algorithms, the performance of the proposed IME method and other routing strategies is evaluated under realistic traffic scenarios. The results demonstrate that the IME method significantly improves message delivery rates and reduces message latency compared to the D-LAR and AODV routing methods. In comparison to the DLAR method, the IME method increases the packet delivery ratio by 4.82%, reduces end-to-end delay by 4.0%, increases throughput by 4.85%, decreases routing overhead by 8.79%, and normalizes routing load by 7.71%. The city of Raipur in Chhattisgarh is the geographical context for this research. Raipur is a rapidly growing urban area with diverse traffic patterns and road conditions. By conducting experiments and simulations in this specific setting, the findings of this study can directly address the challenges faced by VANETs in Raipur City.

Criticality dimension-based probabilistic framework to detect near crashes in a roundabout

BackgroundPreventing fatal traffic accidents towards Vision Zero is a challenge for the society. The collection of critical events from video recorded traffic data is of essential value for a better understanding on how and under what circumstances critical situations evolve. Identified behavioral patterns and derived infrastructural measures cannot only help to make driving safer, but also help to mature automated driving functions (ADFs) to make automated vehicles drive and interact more like humans especially in challenging situations. One flaw when developing ADFs is the dependency on synthetic simulated traffic scenarios.MethodIn this paper, a novel probability-based framework is proposed allowing to measure the degree of criticality C(d) based on two dimensions explaining risk: severity (delta-v) and proximity (distance).ResultsThis metric is applied on real data of a roundabout. An initial evaluation of it was conducted using both a novel proposed method that takes the reaction of the second vehicle merged into account, and a practical application that shows a potential correlation between the traffic expert's perceived risk and the metric.ConclusionQuantifying risk on each of the collected real traffic scenarios makes testing ADFs possible in further more reliable and significant scenarios like near-crashes.

Towards Reliable Design and Operation of Electric Road Systems for Heavy-Duty Vehicles Under Realistic Traffic Scenarios

Decarbonizing heavy-duty vehicles through electrification is a demanding task due to their large energy and power requirements. One promising solution to address this problem are electric road systems (ERS), which provide dynamic charging and traction power for electric vehicles. While their technological feasibility has been proven in numerous pilot projects, its reliability under realistic traffic scenarios is yet to be thoroughly investigated. In this paper, we define the reliability criteria for ERS and investigate how they are influenced by various realistic traffic scenarios. Then, the relationship between the ERS reliability criteria and the key system parameters such as the DC link voltage, distance between substations and feeding arrangement is analyzed. We find that the continuous current limit is the most critical criterion under heavy traffic conditions. Furthermore, we look into a number of run-time techniques to cope with electrical stress on the system and propose a technique based on successive approximation of the total current demand. We compare and evaluate the effectiveness of the run-time techniques and show that our proposed technique is able to improve ERS utilization while ensuring reliable operation.