Inter-vehicle Distance Research Articles

Co-optimization on Ecological Adaptive Cruise Control and Energy Management of Automated Hybrid Electric Vehicles

Electrified drive systems and eco-driving technologies play a crucial role in promoting energy conservation. Eco-driving for hybrid electric vehicles(HEVs) is an intricate problem involving intertwined speed planning and energy management. In this context, an ecological adaptive cruise control (eco-ACC) and powertrain energy management strategy considering Signal Phase Timing Message (SPaT) can enhance both performance and real-time implementation. Specifically, this study develops a novel co-optimization method based on Pontryagin's minimum principle (PMP) that combines car-following control rules with the SPaT for a parallel HEV. The methodology involves the following steps: firstly, the parallel HEV model is established; secondly, the safe following distance model is constructed and the car-following control rules are devised to ensure safe driving. Subsequently, the co-optimization method based on PMP is then presented to simultaneously optimize the eco-driving problem of an ego-vehicle by converting the inter-vehicle distance constraint of the lead-vehicle into the limitation of the speed of the ego-vehicle. Finally, simulations are conducted under different scenarios for both fused SPaT and non-fused SPaT strategy. The simulation results demonstrate a reduction in fuel consumption by 6.27% and 5.69% in two different scenarios, respectively, and a shorter driving time for the fused SPaT strategy compared to the non-fused SPaT strategy.

Effect of luminance of head-up displays on recognition of visual objects on roads

ObjectiveThe purpose of this study is to investigate the effect of visual obstruction when a head-up display (HUD) is presented in front of a driver's field of view. BackgroundThe use of HUDs is expected to increase due to advances in augmented reality (AR) technology. A conventional HUD is displayed at a relatively low position in relation to the driver's viewpoint and has little effect on their front view. However, while an HUD, such as the AR-HUD, is presented in front of the driver's field of vision and helps them in presenting ADAS information, there is a concern that this display may obscure visual objects on the road from the driver's field of vision. MethodAn experiment using a mockup consisting of a HUD and a driver's seat was conducted with 28 participants. A passenger car, motorcycle, and crossing pedestrian were presented as visual objects on a 65-in monitor behind the image of the HUD. Participants were asked questions under different conditions with changes in the HUD luminance and asked to correctly answer questions regarding changes in the inter-vehicle distance and the existence of visual objects on the road. The number of correct answers provided by the participants was recorded. ResultsThe experimental results revealed that the HUD had an effect under the twilight condition wherein the background luminance was low. Furthermore, analyzing the number of correct answers for each visual target revealed that the HUD had an effect under conditions wherein the size of the visual object was small. ConclusionIt was experimentally derived that the luminance ratio Rluminance = 1.1 was the threshold value that affected the recognition of the visual object in the background when the HUD was presented in front of the driver's field of view. ImplicationThe threshold value of the luminance ratio Rluminance obtained in this study can be considered when deciding how to properly display the HUD in front of the driver's field of view.

A resilient event-triggered control strategy for truck platooning cyber–physical systems against denial-of-service attacks

With the deployment of vehicle-to-everything(V2X) communication technology, Denial-of-Service(DoS) attacks gradually pose potential threats for the truck platooning cyber–physical systems(TPCPS) due to disruption of information exchange in vehicular networks, resulting in instability of truck platooning and even traffic accidents. Motivated by this, the study proposes a resilient event-triggered control strategy to maintain the performance or stability of the TPCPS when DoS attacks happen. First, a resilient event-triggered mechanism is proposed to ensure that the onboard controller can receive and update status information in time after attack intervals, mitigating effect of the vehicle-to-vehicle(V2V) communication disruptions. Subsequently, the sufficient condition is derived which is to confine DoS attacks and makes a key role in maintaining the platoon’s internal stability. To guarantee the consensus control performance of the TPCPS, the switched event-triggered controller is designed by the Lyapunov approach. The controller is expected to output corresponding control based on the updated status information in communication interval. Ultimately, the proposed strategy’s effectiveness is validated through simulations. The proposed resilient event-triggered control strategy is shown to be able to effectively mitigate abnormalities in the TPCPS under DoS attacks, thus ensuring safe and comfortable driving. Compared with event-triggered sliding mode control, the proposed method achieves smaller inter-vehicle distances while ensuring stability, enhancing traffic efficiency.

Combined speed and distance control for optimized vehicle platooning

This paper presents a novel approach to vehicle platooning using combined formation-regulation control, aiming to enhance the operation of Connected and Autonomous Vehicles (CAVs). By integrating advanced control algorithms with precise positioning and synchronization tools, our methodology enables CAVs to autonomously navigate while maintaining minimal inter-vehicle distances, thus optimizing convoy synchronization. The proposed system consists of two main controllers: a speed consensus controller to synchronize vehicle speeds and a following distance controller to ensure safety. Our combined control approach combines these controllers dynamically based on real-time vehicle spacing, employing exponential functions to smooth the transition between control modes, thereby preventing abrupt changes in vehicle behavior. This method not only increases energy efficiency and traffic throughput but also significantly reduces the likelihood of accidents caused by sudden braking, contributing to decreased fuel consumption and enhanced road safety. Our work not only advances the technology of vehicle platooning but also promises broader implications for the overall transportation ecosystem.

SESF State-Oriented Vehicle-Following Control Based on Real-Time Tracking of Dynamic Safety Intervehicle Distance

SESF State-Oriented Vehicle-Following Control Based on Real-Time Tracking of Dynamic Safety Intervehicle Distance

Adaptive Speed Control Scheme Based on Congestion Level and Inter-Vehicle Distance

Cellular vehicle-to-everything (C-V2X) enables short-distance communication between vehicles and other users to improve road safety through data sharing. Conventional research on C-V2X typically assumes that vehicles travel at the same speed with a fixed inter-vehicle distance (Disinter). However, this assumption does not reflect the real driving environment or promote road traffic efficiency. Conversely, assigning different speeds to vehicles without a structured approach inevitably increases the collision risk. Therefore, determining appropriate speeds for each vehicle in the C-V2X framework is crucial. To this end, considering the road environment and mobility, this study introduces an adaptive speed mechanism based on the congestion level of a zone and Disinter. First, the given scenario is divided into several zones. Subsequently, based on the congestion level of a zone and the Disinter level, an adaptive speed is defined for each vehicle. This approach ensured that vehicles adopt lower speeds in congested situations to reduce the collision probability and higher speeds in sparse traffic cases to improve traffic efficiency. The performance of the proposed adaptive speed scheme is compared with that of conventional fixed-speed settings. The results show that the adaptive speed control scheme outperforms conventional fixed-speed schemes in terms of the packet reception ratio (PRR) and collision ratio (CR). Specifically, the proposed mechanism can reduce the CR to 0 and ensure that the PRR is higher than 0.98 in low-density scenarios.

Stability and Intervehicle Distance Analysis of Vehicular Platoons: Highlighting the Impact of Bidirectional Communication Topologies

Stability and Intervehicle Distance Analysis of Vehicular Platoons: Highlighting the Impact of Bidirectional Communication Topologies

Developing Fuel Savings Performance Function Considering Various Truck Platooning Configurations

<div>Truck platooning facilitates the operation of trucks in close proximity to one another, resulting in decreased air resistance and improved fuel efficiency. While previous research has mostly focused on the effects of intra-distance on fuel savings, this study aims to develop fuel savings performance functions considering various truck platooning configurations. This article comprehensively investigates the influence of different truck platoon configurations on fuel savings. This analysis focuses on examining the impacts of several variables including inter-vehicle distance, platoon speed, truck weight, number of trucks in the platoon, and the truck’s distinctive design characteristics. Data used in the analysis were collected from 10 different field experiments. Three machine learning techniques—artificial neural networks (ANN), extreme gradient boosting (XGBoost), and K-nearest neighbors (KNN)—alongside the negative binomial regression model were employed. Upon evaluation, the negative binomial regression model emerged as the most accurate, boasting a prediction accuracy of 74%. This high-performing model was subsequently leveraged to derive an equation for estimating fuel savings. The results indicated that the truck platoon’s size is the most significant factor affecting fuel efficiency. Specifically, the inclusion of additional trucks in the platoon leads to substantial fuel savings. Moreover, as the platoon’s speed increases, there is a noticeable increase in fuel savings. The design of the truck plays a role: conventional trucks are more fuel efficient than cab-over trucks. Lastly, the weight of the truck has a minor impact on the platoon’s fuel efficiency. Overall, it is essential to consider multiple variables when evaluating truck platoon arrangements for optimal fuel efficiency.</div>

Effect of additional tasks on the reaction time of braking responses in simulated car driving: beyond the PRP effect.

The presentation of one task increases the reaction time on a subsequent task, if stimulus onset asynchrony (SOA) between tasks is short. This psychological refractory period (PRP) effect is typically leveling off as SOA approaches 1 s, which has been documented both in classical laboratory paradigms and in simulated car driving. Here we report a more persistent effect on the subsequent task that goes well beyond the typical duration of the PRP effect. In a driving simulator, 120 healthy older participants followed a lead car that mostly drove at a constant speed. They had to maintain a regular distance from the lead car and had to brake when the lead car braked. Participants also engaged in several additional tasks during driving (two types of tasks: typing three-digit numbers, stating arguments on public issues). SOA between the braking task and the last preceding additional task was 11.49 s ± 1.99 (mean and standard deviation). In a control condition, the braking task was administered without additional tasks. Main performance outcome was Braking Reaction Time (RT, in s), as the interval between onset of brake lights of the lead car and the moment participants released the gas pedal. Additionally, foot movement time (MT, in s), i.e., the difference between gas pedal release and brake pedal onset, was considered for possible compensation behavior. Inter-vehicle distance to the lead car (in m) was taken into account as a moderator. We found that RT averaged 0.77 s without additional tasks, but averaged 1.45 s with additional tasks. This RT difference was less pronounced at smaller inter-vehicle distances, and was not compensated for by faster MT from the gas pedal to the brake pedal. We conclude that detrimental effects of additional tasks on subsequent braking responses can be more persistent than suggested by the PRP effect, possibly because of maintaining multiple task sets, requiring increased executive control. We further conclude that potential detrimental effects can be ameliorated at small inter-vehicle distances by mobilizing extra cognitive resources when response urgency is higher. As a practical implication of our study, distracting stimuli can have persisting detrimental effects on traffic safety.

Optimal hybrid strategy in adaptive cruise control system for enhanced autonomous vehicle stability and safety

The adaptive cruise control system relies heavily on the vehicle's longitudinal control algorithm. Traditional longitudinal control algorithms typically depend on precise vehicle dynamic modeling or complex controller parameter calibrations. In the research, an optimal hybrid strategy for the control of basic adaptive cruise control system is proposed. The proposed hybrid strategy is the combination of both the Model Predictive Control (MPC) and Coati Optimization Algorithm (COA) and hence it is named as MPCCOA strategy. The fundamental goal of the proposed work is that a vehicle should maintain its current velocity when it approaches another vehicle that is driving at a steady velocity. The inputs for the proposed strategy are vehicle speed and acceleration. The inter-vehicle distance error, velocity error and acceleration are regulated based on the upper and lower limit of acceleration command with the help of proposed strategy. For accomplishing the required steadiness with the limitations in the given input and actual velocity with constant previous vehicle velocity, the proposed controller is utilized. The proposed controller is used to control the traction force of the host vehicle such that the vehicle speed follows the optimal speed trajectory as good as possible while ensuring constraints like distance to a preceding vehicle or speed limits. The performance of the proposed is implemented in the MATLAB platform and compared with existing approaches. The proposed technique demonstrates superior performance metrics, achieving the collision rate, rate of reaching maximum deceleration and ratio of exceeding comfortable deceleration or jerk are 1.27 %, 3.131 % and 9.052 %, respectively.

Real-time sideswipe conflict risk prediction on the expressway

Predicting conflict risk from a microscopic perspective is crucial for understanding traffic dynamics and preventing crashes. However, previous studies often missed this perspective and lacked methods for applying it in spatial contexts. This study introduces a method that predicts conflict risk using both spatial and temporal segments, using real-time data for practical implications. Employing a hybrid model that combines long short-term memory (LSTM) and a convolutional neural network (CNN), the research utilizes three hours of vehicle trajectory data from the Shanghai Inner Ring Expressway. Specifically, a 10 s data window is employed to forecast conflict risk within the subsequent 3 s, 4 s, and 5 s intervals, considering a 10 m spatial section. Modified time to collision (MTTCL ) is adopted to assess conflicts between vehicles. The study incorporates 12 factors to predict conflict risk, prioritizing them based on SHAP values. The results highlight that most conflicts occur within the 3 s prediction horizon, with prediction accuracy decreasing as the forecast horizon extends. Notably, the study identifies key contributing factors, including the speed difference between vehicles, the number of lane changes, inter-vehicle distance, distance variability, and the number of cars. Spatially, the research identifies entry and exit ramps and their surrounding areas on the expressway as hotspots for sideswipe conflicts.

Investigation of Different Communication Topologies for Cooperative Adaptive Cruise Control Systems

After Adaptive Cruise Control (ACC) system applications, Cooperative Adaptive Cruise Control (CACC) systems are becoming an important part of automotive technology and industry in autonomous vehicles convoy applications. Together with this developing technology, CACC systems use vehicle to vehicle (V2V) communication to automatically transmit the movement information of vehicles. In this context, ACC systems use Radar or LIDAR measurements while CACC systems also consider the acceleration of the preceding vehicle. In this paper, the forms of information transmission between vehicles in autonomous vehicle convoys using CACC systems have been examined. From these forms of information transmission, the leader following, the predecessor follow-ing and the leader – predecessor following topologies have been considered. For each topology, an autonomous vehicle convoy consisting of eight vehicles was modeled in the MATLAB/Simulink environment. The feedforward and the feedback control system structure were given for CACC and ACC systems. For different communication topologies, the position-time, the speed-time, the acceleration-time and the headway time-time results were obtained. The maximum intervehicle distance error plots for each vehicle in different topology convoys were given to analyze the dynamic behavior of the convoys. The results have been analyzed in terms of the maximum intervehicle distance, the maximum speed, the minimum and the maximum acceleration, and the maximum headway time deviation from the desired headway time.

Decentralized platoon formation for a fleet of connected and autonomous trucks

This research explores energy-saving potential through platooning, a method aimed at reducing energy consumption, especially for connected and autonomous vehicles allowing shorter inter-vehicle distances. In platooning, energy is saved due to reduced drag, but there is increased energy use during acceleration and deceleration while forming and maintaining platoons. A decentralized game theory model is created to assess energy-saving capabilities in typical traffic scenarios. This model is a dynamic game where drivers act independently, deciding whether to join a platoon to maximize individual gains. A simulation model is developed to replicate vehicle behavior in realistic traffic conditions, estimating energy consumption over time. Findings show that decentralized decision-making leads to system-wide energy savings of 2.2% to 2.6%, contrasting with a 3% saving achieved through centrally mandated platoon formation.

STABILITY ANALYSIS OF VEHICLE BEHAVIOR IN A LONG PLATOON

A vehicle platoon is the method of the group of vehicles traveling in short inter-vehicle distance. It can increase the traffic capacity in keeping the traffic safety. Since, in the platoon of vehicles, the vehicles travel in short inter-vehicle distance, the traffic safety depends on the vehicle distance. In this paper, the vehicle velocity is controlled according to the car-following models. The vehicles travelling at the rear of the platoon refer to their frontal vehicles for controlling their velocity. The stability analysis of the models leads the adequate range of the model parameters in which vehicles can control the vehicle distance safely without crush to the preceding vehicle. The stability analysis reveals that the stable region of the parameters becomes wider according to the increase of the number of the frontal vehicles. Finally, the simulation results show that the stability analysis can give the adequate value of the model parameter.

Enhanced Routing Protocol for Health Monitoring in Urban Traffic Networks via VANET: Analyzing IW-GeoR Against WGeoR

The primary aim of this initiative is to establish an effective routing system for managing both human-induced and natural calamities using a Wireless Body Sensor Network (WBSN) integrated with VANET within urban traffic networks. This is accomplished through a comparison between the Weighed Geographic Routing (WGeoR) protocol and the Improved Weighed Geographical Routing (IWGeoR) protocol, targeting minimized packet loss and reduced delay. The IWGeoR protocol stands out by efficiently selecting cluster heads to mitigate delay and packet loss, considering various factors such as differences in vehicle speeds, inter-vehicle distances, and traffic mobility. The protocol’s effectiveness relies on innovative node degrees, channel quality, proximity factors, and communication connection expiration times. For this study, twenty samples (n=20) were gathered from each of the two groups using diverse vehicles. Clinical.com established a pre-test power of 80% (G-power) for each group, with alpha and beta coefficients set at 0.05 and 0.2 respectively, in order to assess the protocol’s performance in terms of packet loss and average latency. The simulation outcomes indicate that the IWGeoR protocol surpasses the WGeoR protocol in crucial metrics such as packet loss and mean delay. Specifically, the IWGeoR protocol reduces packet loss by 10.77% and delay by 31.03%. The Independent Sample T-test calculates a significant p-value of 0.001 for the IWGeoR and WGeoR protocols, demonstrating statistical significance (p<0.005). Based on experimental findings and the Independent Sample T-test, the proposed IWGeoR protocol has demonstrated superior performance compared to the WGeoR protocol.

Co-optimization of Energy Management and Eco-Driving Considering Fuel Cell Degradation Via Improved Hierarchical Model Predictive Control

An advanced eco-driving technology is widely recognized as having enormous potential to reduce the vehicle fuel consumption. However, most research on eco-driving focuses on the stability and safety for vehicle operating while disregarding its comfort and economy. To meet the requirements for safety and comfort, at the same time, enhance the economic performance of the vehicles, an improved hierarchical model predictive control cooperative optimization strategy is proposed for fuel cell hybrid electric vehicle with car-following scenario. Specifically, the upper-level model predictive controller controls the velocity, inter-vehicle distance and acceleration to guarantee safety and comfort for driving. According to the velocity information obtained from the upper model predictive controller, the lower-level improved model predictive controller considers the impact of disturbance changes on vehicle economy and aims to minimize the vehicle operating cost considering fuel cell degradation, so as to allocate energy rationally. Finally, the enhancement of economic performance of proposed strategy is verified with the results of comparative study that 3.09% economic improvement on the premise of assuring safety and comfort of driving.

Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions

Abstract This paper analyzes the intelligent networked fleet formation mechanism under adaptive cruise control (CACC). By constructing a communication architecture system including network layer, coordination layer, control layer and physical layer, and based on environment sensing technology and 5G network, a layered control structure for autonomous driving vehicles is established. In the study, the inter-vehicle distance control strategy is proposed, and combined with the theory of metacellular automata, the study constructs an intelligent networked fleet model, which adopts the full speed difference model and adaptive cruise control vehicle following model. The simulation analysis is carried out through the actual data of a section of G6 Beijing-Tibet Expressway, and the simulation environment is a two-lane highway of 3000m, considering different CAV vehicle penetration rates (0% to 100%). The results show that with the increase of CAV penetration, the traffic flow and the congestion significantly improved. The intelligent Connected Vehicles fleet performs well in the obstacle avoidance task, effectively reducing unnecessary lane changes and shortening the congestion time compared to the conventional system, with the distance error controlled within 1m. This study shows that the intelligent connected vehicles-connected fleet has significant advantages in improving traffic flow’s operational efficiency and safety.

PID-Based Longitudinal Control of Platooning Trucks

This article focuses on the development and assessment of a PID-based computationally cost-efficient longitudinal control algorithm for platooning trucks. The study employs a linear controller with a nested architecture, wherein the inner loop regulates relative velocities while the outer loop governs inter-vehicle distances within platoon vehicles. The design of the proposed PID controller entails a comprehensive focus on system identification, particularly emphasizing actuation dynamics. The simulation framework used in this study has been established through the integration of TruckSim® and Simulink®, resulting in a co-simulation environment. Simulink® serves as the platform for control action implementation, while TruckSim® simulates the vehicle’s dynamic behavior, thereby closely replicating real world conditions. The significant effort in fine-tuning the PID controller is described in detail, including the system identification of the linearized longitudinal dynamic model of the truck. The implementation is followed by an extensive series of simulation tests, systematically evaluating the controller’s performance, stability, and robustness. The results verify the effectiveness of the proposed controller in various leading truck operational scenarios. Furthermore, the controller’s robustness to large fluctuations in road grade and payload weight, which is commonly experienced in commercial vehicles, is evaluated. The simulation results indicate the controller’s ability to compensate for changes in both road grade and payload. Additionally, an initial assessment of the controller’s efficiency is conducted by comparing the commanded control efforts (total torque on wheels) along with the total fuel consumed. This initial analysis suggests that the controller exhibits minimal aggressive tendencies.

Orthogonal Space-Time Block Coding for Double Scattering V2V Links with LOS and Ground Reflections

This work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground reflections and an increased Doppler frequency (time-variant channels). STBCs of various coding rates (using an approximation model) are evaluated by assuming antenna elements distributed over the surface of two contiguous vehicles. A multi-ray model is used to study the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx–Rx antenna links considering ground reflections. A double scattering model is used to include the effects of stochastic channel components that depend on the Doppler frequency. The results show that STBCs are capable of counteracting fades produced by destructive self-interference components across a range of inter-vehicle distances and for a range of Doppler frequency values. Notably, the effectiveness of STBCs in deep fades is shown to outperform schemes with exclusive receive diversity, despite the interference created by the loss of orthogonality in time-varying channels with a moderate increase of Doppler frequency (mainly due to higher vehicle speeds, higher frequency or shorter time slots). Higher-order STBCs with rate losses are also evaluated using an approximation model, showing interesting gains even for low coding rate performance, particularly when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in time-varying vehicular channels with modified LOS.

Co-optimization of speed planning and cost-optimal energy management for fuel cell trucks under vehicle-following scenarios

Fuel cell hybrid electric heavy-duty vehicles play a key role to realize green and low-carbon travel. To cope with actual traffic environment, an advanced energy management strategy (EMS) is crucial to ensure vehicle’s efficiency and economy. In this paper, a predictive co-optimization control method is designed to achieve speed planning and energy management for the host vehicle with a fuel cell/Li-ion battery hybrid energy storage system under vehicle-following scenarios. Firstly, the host vehicle obtains the real-time speed of leading vehicle by communication technology, and speed prediction for the leading vehicle is implemented by fuzzy C-means clustering enhanced Markov Chain (FCM-MC) considering driving habits. Secondly, based on speed prediction results, the speed planning which aims to ensure safe inter-vehicle distance and minimize the demanded vehicular power is implemented to derive the speed curve of host vehicle. Finally, according to speed planning results, predictive energy management is applied to achieve power allocation between fuel cell (FC) and battery. The simulation results denote that the proposed speed prediction method can decrease forecast error effectively. The speed planning ensures the safe inter-vehicle distance. With speed prediction accuracy improved, the proposed hierarchical energy management strategy can reduce fuel cell degradation cost by 1.31%-3.48% and total operation cost by 0.75%-1.94%.