Vehicles In Scenarios Research Articles

Vehicle Clustering and Resource Allocation Algorithm Based on Cellular Network

<p>As a special Mobile Ad-hoc Network (MANET), Vehicular Ad-hoc Network (VANET) plays a very important role in the future intelligent transportation system. In order to solve the problems of unstable communication connection, fast network topology change and low communication resource utilization caused by high vehicle mobility in VANET, a low-complexity resource allocation algorithm based on vehicle cluster is proposed. Firstly, considering the speed, position and moving direction of the vehicles, a vehicle clustering algorithm based on movement consistency is proposed to cluster the vehicles and keep the vehicle cluster stable. Secondly, a low-complexity resource allocation algorithm is proposed to improve the utilization rate of communication resources, which is constrained by the interference caused by the vehicle clusters to the cellular users. Simulation results show that the proposed algorithm has low complexity and can better maintain the stability of vehicle clusters and improve the system capacity in the common complex Internet of Vehicles (IoV) scenarios in cities.</p> <p> </p>

Implementation and Performances Evaluation of Advanced Automotive Lateral Stability Controls on a Real-Time Hardware in the Loop Driving Simulator

This study concerns the comparative investigation of two advanced lateral stability automotive controllers with respect to a commercial solution. The research aims to improve the stability performances achieved by a combined tracking of yaw rate and side-slip angle through the application of optimal efforts. The proposed solutions are based on Linear Quadratic Regulation and Sliding Mode Control, respectively. Both rely on the same approach for the control objective definition but differ from the action perspective. This solution involves the adoption of a differential braking actuation technique to deliver a desired yaw moment to the car body to track controlled states. Indeed, a sliding controller can also traction torques of hub-motor configurations as well as steering corrections, achieving vehicle stability and a driving response in accordance with the pilot’s intentions. Calibration and validation of the controllers are performed through a Hardware-in-the-Loop simulation rig, along with a real-time static simulator, performing different close-loop maneuvers to assess achievements in terms of lateral stability. Results show that both solutions ensure higher handling performances if compared to Non-controlled or Commercial-controlled vehicle scenarios.

Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm

In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires a high task processing delay, and due to the high delay of cloud computing to upload computing tasks to the cloud, the MEC server has limited computing resources, which will increase the task processing delay when there are more tasks. To solve the above problems, a vehicle computing network based on cloud-edge-end collaborative computing is proposed, in which cloud servers, edge servers, service vehicles, and task vehicles themselves can provide computing services. A model of the cloud-edge-end collaborative computing system for the Internet of Vehicles is constructed, and a computational offloading strategy problem is given. Then, a computational offloading strategy based on the M-TSA algorithm and combined with task prioritization and computational offloading node prediction is proposed. Finally, comparative experiments are conducted under task instances simulating real road vehicle conditions to demonstrate the superiority of our network, where our offloading strategy significantly improves the utility of task offloading and reduces offloading delay and energy consumption.

Electric vehicle fleet penetration helps address inequalities in air quality and improves environmental justice

Accelerated penetration of on-road electric vehicles offers regional and community-scale air quality benefits; however, such benefits have not been previously quantified regarding environmental justice communities near major roads. This study evaluated six 2040 electric vehicle scenarios and quantified concentration reductions of nitrogen dioxide and fine particulate matter (diameter less than 2.5 µm) for southern California environmental justice communities near Interstate 710. Findings showed that aggressive electric vehicle penetration (85% electric vehicle share) reduced nitrogen dioxide and fine particulate matter concentrations more in communities with more people of color (1.9 ppb and 1.1 μg m−3) than in communities with more White residents (1.6 ppb and 0.94 μg m−3). Aggressive electric vehicle penetration reduced pollution exposure disparity by 30% for nitrogen dioxide and 14% for fine particulate matter. Disparity reductions were also found based on educational attainment. Results suggest policies that encourage accelerated electric vehicle penetration will address inequalities in air pollution and help achieve environmental justice.

Joint Task Offloading and Resource Allocation for Vehicular Edge Computing Based on V2I and V2V Modes

In an internet of vehicle (IoV) scenario, vehicular edge computing (VEC) exploits the computing capabilities of the vehicles and roadside unit (RSU) to enhance the task processing capabilities of the vehicles. Resource management is essential to the performance improvement of the VEC system. In this paper, we propose a joint task offloading and resource allocation scheme to minimize the total task processing delay of all the vehicles through task scheduling, channel allocation, and computing resource allocation for the vehicles and RSU. Different from the existing works, our scheme: 1) considers task diversity by profiling the tasks of the vehicles by multiple attributes including data size, computation amount, delay tolerance, and task type; 2) considers vehicle classification by dividing the vehicles into 4 sets according to whether they have task offloading requirements or provide task processing services; 3) considers task processing flexibility by deciding for each vehicle to process its tasks locally, to offload the tasks to the RSU via V2I (Vehicle to Infrastructure) connections, or to the other vehicles via V2V (Vehicle to Vehicle) connections. An algorithm based on the Generalized Benders Decomposition (GBD) and Reformulation Linearization (RL) methods is designed to optimally solve the optimization problem. A heuristic algorithm is also designed to provide the sub-optimal solution with low computational complexity. We analyze the convergence and complexity of the proposed algorithms and conduct extensive simulations in 6 scenarios. The simulation results demonstrate the superiority of our scheme in comparison with 4 other schemes.

Machine Learning Insights for Behavioral Data Analysis Supporting the Autonomous Vehicles Scenario

The advent of the digital innovation era is changing service, use, and resources management paradigms, offering a wide range of new and essential opportunities. In particular, the advent of the Internet of Things (IoT), i.e., the ability to connect individual objects to the Internet, also capable of communicating autonomously, has its particular declination on the connected vehicle. It is combined with the potential of advanced sensors placed pervasively on vehicles, which offer multifunctional monitoring capabilities of the entire system: from individual components up to the whole vehicle, including driver behavior and conditions and many exogenous parameters to the vehicle (road and weather conditions, congestion, risk situations, changes to mobility plans, etc.). In this perspective, machine learning (ML) models can transform raw data into new knowledge; they can contribute in an innovative way to define and suggest decisions, strategies, and criteria for resource use. Nowadays, most intelligent mobility projects also integrate artificial intelligence (AI) and ML solutions. In this article, we present and discuss the application of unsupervised learning techniques on a vehicular IoT data set. The main goal is to generate new knowledge about a geographical zone by analyzing historical drivers behavioral data. The autonomous vehicle’s framework can exploit the generated valuable insights to optimize the routes and prevent critical issues.

Air quality improvement through vehicle electrification in Hainan province, China

To improve the ecological environment, provinces in China have set ambitious goals for the electrification of fossil-fuel-powered vehicles (FVs) and the promotion of electric vehicles (EVs). Hainan is the first province to propose a clean energy target that schedules the banning of new FVs sales from 2030. Therefore, Hainan is a good case study to illustrate how this policy might improve regional air quality over the coming years. This study first developed an anthropogenic emission inventory of seven major air pollutants in 2017 in Hainan. The total emissions of CO, NOx, NH3, volatile organic compounds (VOCs), PM10 and PM2.5 and SO2 in 2017 were estimated as 247.56, 69.61, 61.87, 41.38, 37.02, 19.82, and 8.55 kt, respectively. Using the developed emission inventory, multiple scenarios of economic development were considered to assess the benefits to air quality from Hainan's goal of electrification. In comparison with 2017, the reductions in emissions of SO2, NOx, CO, PM10, PM2.5, VOCs, and NH3 by 2045 were projected to be 5.45 (11.11%), 275.07 (57.32%), 675.51 (34.07%), 8.39 (5.73%), 7.73 (8.24%), 81.15 (9.76%), and 4.89 (0.91%) kt, respectively, under the all-electric vehicle scenarios. These results indicate that this policy will not only reduce the emission of air pollutants but also avoid complex O3 pollution in the future. The findings of this work elucidate the effects of vehicle electrification policies on regional air quality and provide scientific support for policymakers in developing pollution control strategies.

How Can the Trust-Change Direction be Measured and Identified During Takeover Transitions in Conditionally Automated Driving? Using Physiological Responses and Takeover-Related Factors.

This paper proposes an objective method to measure and identify trust-change directions during takeover transitions (TTs) in conditionally automated vehicles (AVs). Takeover requests (TORs) will be recurring events in conditionally automated driving that could undermine trust, and then lead to inappropriate reliance on conditionally AVs, such as misuse and disuse. 34 drivers engaged in the non-driving-related task were involved in a sequence of takeover events in a driving simulator. The relationships and effects between drivers' physiological responses, takeover-related factors, and trust-change directions during TTs were explored by the combination of an unsupervised learning algorithm and statistical analyses. Furthermore, different typical machine learning methods were applied to establish recognition models of trust-change directions during TTs based on takeover-related factors and physiological parameters. Combining the change values in the subjective trust rating and monitoring behavior before and after takeover can reliably measure trust-change directions during TTs. The statistical analysis results showed that physiological parameters (i.e., skin conductance and heart rate) during TTs are negatively linked with the trust-change directions. And drivers were more likely to increase trust during TTs when they were in longer TOR lead time, with more takeover frequencies, and dealing with the stationary vehicle scenario. More importantly, the F1-score of the random forest (RF) model is nearly 77.3%. The features investigated and the RF model developed can identify trust-change directions during TTs accurately. Those findings can provide additional support for developing trust monitoring systems to mitigate both drivers' overtrust and undertrust in conditionally AVs.

Physical Layer Authentication in the Internet of Vehicles based on Signal Propagation Attribute Prediction

Physical layer authentication (PLA) has emerged as a promising alternative to complex cryptographic-based authentication schemes, especially for the Internet of Vehicles (IoV) scenarios with resource-limited onboard units (OBUs). However, the existing PLA schemes securing the IoV against GPS location spoofing/falsification attacks consider only insider attackers. Moreover, they cannot be used by mobile vehicles to validate GPS locations. To address these issues, this paper proposes a PLA scheme based on the Gaussian process (GP) path loss prediction, where channel state information (CSI) is used to track the variation of the channel characteristics and predict the next legitimate path loss (PL) of the signal from a transmitter for authentication. The key ideas in the proposed scheme are to first establish a mapping between the historical CSI attributes and PL features of the transmitter’s signal and use this mapping to predict the next PL, which is then used to cross-verify the transmitter’s reported location information. Extensive simulation experiments are conducted using generated radio channel characteristics from the quasideterministic radio channel generator (QuaDRiGa) to demonstrate the effectiveness of the proposed approach. The results of the experiments show that our system efficiently addressed the limitations of the existing works and improves the authentication performance in IoV environments.

Nonlinear Model Predictive Control: an Optimal Search Domain Reduction

Nonlinear Model Predictive Control (NMPC) is a powerful control method, used in many industrial contexts. NMPC is based on the online solution of a suitable Optimal Control Problem (OCP) but this operation may require high computational costs, which may compromise its implementation in “fast” real-time applications. In this paper, we propose a novel NMPC approach, aiming to improve the numerical efficiency of the underlying optimization process. In particular, a Set Membership approximation method is applied to derive from data tight bounds on the optimal NMPC control law. These bounds are used to restrict the search domain of the OCP, allowing a significant reduction of the computation time. The effectiveness of the proposed NMPC strategy is demonstrated in simulation, considering an overtaking maneuver in a realistic autonomous vehicle scenario.

Correlation Filter of Multiple Candidates Match for Anti-Obscure Tracking in Unmanned Aerial Vehicle Scenario

Due to the complexity of Unmanned Aerial Vehicle (UAV) target tracking scenarios, tracking drift caused by target occlusion is common and has no suitable solution. In this paper, an occlusion-resistant target tracking algorithm based on the correlated filter tracking model is proposed. First, instead of the traditional target tracking model that uses single template matching to locate the target, we locate the target by finding the optimal match based on multiple candidates templates matching. Then, in order to increase the accuracy of matching, we use the self-attentive mechanism for feature enhancement. We experiment our proposed algorithm on datasets OTB100 and UAV123, respectively, and the results show that the tracking accuracy of our algorithm outperforms the traditional correlated filtered target tracking model. In addition, we have also tested the anti-occlusion performance of our proposed algorithm on some video sequences in which the target is occluded. The results show that our proposed algorithm has a certain resistance to occlusion, especially in the UAV tracking scenario.

Integrated Longitudinal and Lateral Hierarchical Control of Cooperative Merging of Connected and Automated Vehicles at On-Ramps

Connected and automated vehicles (CAVs) can improve traffic safety and transportation network efficiency while also reducing environmental impacts. However, congestion and accidents can easily occur at merging roadways. Therefore, coordinating cooperative merging of CAVs is one of the most common traffic management problems. This paper addresses the problem of integrated longitudinal and lateral cooperative merging control with practical implications for CAVs approaching on-ramps. A hierarchical and decentralized cooperative coordination framework was developed to systematically control the merging of CAVs. The control system of each vehicle can be divided into an upper-level and lower-level. For upper-level control, an optimal control-based algorithm considering input constraints was presented to optimize fuel consumption and passenger comfort. A decision strategy was developed to optimize the start time of lateral trajectory planning. To achieve lower-level control, a Proportional-Integral (PI) controller was used for tracking the optimized longitudinal speed of the upper-level and a decentralized unified algorithm based on nonlinear model predictive control was proposed for tracking the upper-level optimal trajectory. To avoid lateral collision, the driving safety field based on vehicle size and motion state was selected as one of tracking the optimization objectives. Efficiency of the proposed framework and the algorithm was validated by CarSim/Simulink co-simulations of near-real-world vehicle scenarios. The proposed integrated merging control system can improve traffic efficiency and reduce fuel consumption compared to baseline with the potential for real-world application. Furthermore, the results demonstrate the potential applicability of cooperative control methods based on upper-level vehicle control.

Evolutionary game analysis of pedestrian-autonomous vehicle interactions at unsignalized road sections: a policy intervention perspective

ABSTRACT Focusing on the crossing scenarios of pedestrians and autonomous vehicles (AVs) at unsignalized road sections, this study proposes an evolutionary game model considering the related benefits of AVs, pedestrians and traffic managers, and it explores the impact of policy intervention. The game analysis results indicate that strategies of the three agents are closely associated with pedestrians’ trust in driverless technology, human-vehicle communication and government regulation. In cases where regulation exists, increasing the AV supervision threat would facilitate the equilibrium to evolve toward pedestrians crossing and AVs yielding. In addition, we unexpectedly find that the introduction of crash probability information cannot boost the equilibrium outcomes in the expected direction, which may be related to information overload. Finally, this study verifies the validity of the model through numerical simulations and provides managerial implications with respect to driverless technology promotion, communication device design and regulatory policy formulation.

Improved estimation of pollutant emissions from agricultural machinery and projection of its reduction potential in the Beijing-Tianjin-Hebei region, China

As an important non-road mobile source, agricultural machinery has gradually become an important source of air pollution in China. However, research on the emission factors and mitigation potential of agricultural machinery is still in its infancy. In this study, the emission characteristics of ten common agricultural machinery in the Beijing-Tianjin-Hebei (BTH) region were measured using a portable emission measurement system (PEMS). The results show that the instantaneous emission of pollutants from agricultural machinery increased with an increase in engine power. Nitrogen oxide (NOX) emissions were 2.2 and 3.9 times higher in moving and working modes, respectively, than that in idling. The measured hydrocarbon (HC), carbon monoxide (CO), NOX, and particulate matter (PM2.5) emission factors were 0.99, 0.94, 1.12, and 0.95 times, respectively, higher than the recommended values. The CO, HC, NOX, and PM2.5 emissions from agricultural machinery in the BTH region in 2020 were 134.83, 28.54, 182.51, and 15.40 Kt, respectively. Based on the newly established inventory, four scenarios were set up to analyse the emission reduction potential. Under the integrated scenario (IS), the emission reduction potential of pollutants reaches the maximum, and the emission reduction in HC, CO, NOX, and PM2.5 in 2030 was 62.33%, 64.41%, 49.19%, and 70.94%. For single control measures, the update emission standards scenario (UES) has the best emission reduction effect of the four pollutants, the controls on in-use vehicles scenario (CIV) has the best emission reduction effect on NOX and PM2.5. These results can provide fundamental information for the development of agricultural machinery emission control policies.

Emergence of norms in interactions with complex rewards

Autonomous agents are becoming increasingly ubiquitous and are playing an increasing role in wide range of safety-critical systems, such as driverless cars, exploration robots and unmanned aerial vehicles. These agents operate in highly dynamic and heterogeneous environments, resulting in complex behaviour and interactions. Therefore, the need arises to model and understand more complex and nuanced agent interactions than have previously been studied. In this paper, we propose a novel agent-based modelling approach to investigating norm emergence, in which such interactions can be investigated. To this end, while there may be an ideal set of optimally compatible actions there are also combinations that have positive rewards and are also compatible. Our approach provides a step towards identifying the conditions under which globally compatible norms are likely to emerge in the context of complex rewards. Our model is illustrated using the motivating example of self-driving cars, and we present the scenario of an autonomous vehicle performing a left-turn at a T-intersection.

Deployment planning tool for environmentally sensitive heavy-duty vehicles and fueling infrastructure

A novel planning tool is introduced for projecting vehicle and fueling infrastructure deployment of near-zero and zero emission heavy-duty vehicles (HDVs). To address climate change and air quality goals, the tool is based on a systematic methodology to develop rollout scenarios that meet environmental goals at the lowest cost, and identify policy to facilitate the deployment. Called TRACE (Transportation Rollout Affecting Cost and Emissions), the methodology provides a framework for developing and analyzing scenarios that consider the technical and socio-political constraints and incentives in the emerging HDV sector. For the California Greenhouse Gas (GHG) Scenario requiring an 80% reduction in 1990 GHG levels by 2050, TRACE projects a significant population of biomass-sourced renewable diesel-fueled internal combustion vehicles, complemented by a substantial population of battery and fuel cell electric powertrain adoption by 2050. In contrast, the Zero Emission Vehicle Scenario projects a near-complete (100%) adoption of battery and fuel cell electric powertrains by 2050 with a 0.53% higher cumulative cost than the GHG Scenario. The policy implications include a need for (1) near-term benefits of low/negative carbon intensity biomass without diminishing adoption of zero emission HDVs, and (2) both renewable fuel and fuel cell electric vehicle incentives tailored to vocational demand.

Research on the Safety Characteristics of Mixed Traffic Flow under Different Penetration Scenarios of Autonomous Vehicles

Research on the Safety Characteristics of Mixed Traffic Flow under Different Penetration Scenarios of Autonomous Vehicles

A NSGA-II variant for the dynamic economic emission dispatch considering plug-in electric vehicles

The dynamic economic emission dispatch considering plug-in electric vehicles (DEED-PEV) belongs to a nonlinear constrained optimization problem and has the multimodal and discontinuity characteristics. The first characteristic mainly comes from the cost function with valve-point effects, and the second one is associated with the constraints of prohibited operating zones. In this paper, a novel NSGA-II (NNSGA-II) is proposed to deal with DEED-PEV. NNSGA-II replaces the simulated binary crossover operator by the one based on Gaussian distribution, and adaptively adjusts the crossover rate of each individual according to its rank in population. Also, it incorporates a rewarding coefficient into the crowding distance in density estimation, which gives considerations to both density and evenness of solutions. Accordingly, NNSGA-II is able to perform extensive searches in solution space and achieve a series of individuals with high diversity and good evenness. Experimental results suggest that NNSGA-II outperforms the other seven MOEAs for nine DEED-PEV problems with different charging and discharging scenarios of plug-in electric vehicles, and its obtained solutions are superior to those of the other seven MOEAs according to the hypervolume and coverage indicators. In short, NNSGA-II is a potential alternative for minimizing the generation cost and pollutant emission of DEED-PEV.

Output Power Regulation of a Series-Series Inductive Power Transfer System Based on Hybrid Voltage and Frequency Tuning Method for Electric Vehicle Charging

In the inductive charging scenario of an electric vehicle (EV), the charger should have the capability to flexibly regulate the output power regardless of the misalignment and EV battery voltage. This article proposes a hybrid voltage and frequency tuning strategy for a series-series (SS) inductive power transfer (IPT) system to regulate power through 0 to 3.3 kW at any battery voltages and misalignment conditions without using additional converters. A voltage tuning is used to achieve full-power of 3.3 kW and partly contribute to reducing the power. When the input voltage achieves the lower limit, a frequency tuning is used to further decrease the power to 0 W, achieving the regulation through 0 to full power. The practical charging region is specified by 110 mm airgap, 100 mm misalignment in both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> directions, and 180–240 V battery voltage range. Experiments are carried out at various conditions, and it shows that with an input voltage range of 180–380 V and a frequency of 82.5–87.9 kHz, the charging power can be regulated between 100 W and 3.3 kW at any case in the specified working region. In addition, a peak dc-dc efficiency of 95.7% is achieved at 3.29 kW.

Edge AIBench 2.0: A scalable autonomous vehicle benchmark for IoT–Edge–Cloud systems

Many emerging IoT–Edge–Cloud computing systems are not yet implemented or are too confidential to share the code or even tricky to replicate its execution environment, and hence their benchmarking is very challenging. This paper uses autonomous vehicles as a typical scenario to build the first benchmark for IoT–Edge–Cloud systems. We propose a set of distilling rules for replicating autonomous vehicle scenarios to extract critical tasks with intertwined interactions. The essential system-level and component-level characteristics are captured while the system complexity is reduced significantly so that users can quickly evaluate and pinpoint the system and component bottlenecks. Also, we implement a scalable architecture through which users can assess the systems with different sizes of workloads.We conduct several experiments to measure the performance. After testing two thousand autonomous vehicle task requests, we identify the bottleneck modules in autonomous vehicle scenarios and analyze their hotspot functions. The experiment results show that the lane-keeping task is the slowest execution module, with a tail latency of 77.49 ms for the 99th percentile latency. We hope this scenario benchmark will be helpful for Autonomous Vehicles and even IoT–edge–Cloud research. Now the open-source code is available from the official website https://www.benchcouncil.org/scenariobench/edgeaibench.html.