Adaptive Cruise Control Research Articles

Optimizing road safety: integrated analysis of motorized vehicle using lattice ordered complex linear diophantine fuzzy soft set.

In this manuscript, we delve into the realm of lattice ordered complex linear diophantine fuzzy soft set, which constitutes an invaluable extension to the existing Fuzzy set theories. Within this exploration, we investigate basic operations such as and , together with their properties and theorems. This manuscript is more amenable in two ways, i.e., it enables real-life problems involving parametrization tool and applications with an existing order between the components of the parameter set based on the preference in the complex frame of reference. Adaptive cruise control (ACC) is a system designed for maintaining distance between two vehicles and to sustain a manually provided input speed. The purpose of cars with ACC is to avoid a collision that frequently happens nowadays, thereby improving road safety regulations amidst rising collision rates. The fundamental aim of this manuscript is to prefer an applicable car with ACC together with its latest model by defining a peculiar postulation of lattice ordered complex linear diophantine fuzzy soft set . Emphasizing real-life applicability, we illustrate the effectiveness and validity of our suggested methodology in tackling current automotive safety concerns, providing useful guidance on reducing challenges related to contemporary driving conditions.

Fuzzy adaptive cruise control with model predictive control responding to dynamic traffic conditions for automated driving

Traditional Adaptive Cruise Control (ACC) systems often struggle to dynamically adapt to rapidly changing traffic conditions, resulting in suboptimal performance. Additionally, with fuel consumption emerging as a critical consideration alongside safety, there is a pressing need for more advanced solutions. This paper presents a novel approach to address these challenges by integrating Fuzzy ACC with Model Predictive Control, denoted as FACMPC. This integration aims to enhance both the longitudinal safety of AVs and fuel efficiency by considering real-time traffic conditions. The FACMPC system utilizes fuzzy logic inside the MPC, adaptively generates controller's weighting factors, allowing the system to adapt instantly to varying traffic environments and driving circumstances. The findings show that this adaptation improves the balance between driving safety, efficiency, and comfort. Additionally, three interruption scenarios, Alpha, Beta and Gama, are examined. In Alpha, the study evaluates the sensitivity of the FACMPC to disturbances by applying band-limited white noise to the lead vehicle velocity. In Beta, the AV experiences a loss of the lead vehicle velocity signal for a defined period, prompting safety considerations and assumptions. The Gama scenario includes a sensitivity analysis to account for variations and uncertainties in parameters by considering a range of ±5% around the nominal values for four key parameters: road slope, wind speed, wind direction, and rolling resistance. The findings indicate that the proposed controller's mean fuel consumption is 8.110, only a 3.21% increase over the nominal, compared to a 7.03% increase for the conventional ACC, demonstrating greater robustness against uncertainties.

Dynamic regressor extension and mixing control Lyapunov functions and high-order control Barrier functions

This paper studies the safety-critical tracking control problem for unknown structured systems with bounded disturbances and high relative degree. Firstly, a dynamic regressor extension and mixing high-order control barrier function (DREM-HOCBF) is constructed with a DREM-based robust finite-time parameter identification law. Our approach directly enforces forward invariance of the system containing parameter estimates and worst-case estimation errors to ensure system safety against parameterised uncertainty. Due to DREM, this method can approach the safety set boundary and reduce the conservatism. Meanwhile, it's also applicable to higher relative degree constraints that are more common in actual systems. Then, a DREM control Lyapunov function (DREM-CLF) that leverages the same worst-case estimation errors is proposed to guarantee that the tracking errors converge exponentially. Moreover, the method's robustness against bounded disturbances is considered. Finally, the feasibility of the developed method is verified via an adaptive cruise control (ACC) problem with unknown parameters and bounded disturbances.

A novel car-following model for adaptive cruise control vehicles using enhanced intelligent driver model

ABSTRACT This paper proposes Enhanced Intelligent Driver Model for Adaptive Cruise Control (EIDM-ACC) vehicles, a novel car-following model that dynamically adjusts desired speed and considers acceleration inertia. The EIDM-ACC model is compared with two widely used models for simulating ACC vehicles – the ACC model developed by the PATH Project (PATH-ACC) at the University of California Transportation Institute and Continuous Asymmetric Optimal Velocity Relative Velocity (CAOVRV) model. Three models are calibrated and cross-validated using real vehicle trajectory data from the OpenACC dataset. Results show that the EIDM-ACC outperforms the other two models in small and large fluctuation stages. In addition, EIDM-ACC has better performance in capturing the instability and energy consumption of ACC vehicles, and also has advantages over the other two models in terms of safety.

The calculation and distribution of CAV carbon emissions on urban transportation systems: A comparative analysis of renewable and non-renewable energy sources

When powered by electricity, Connected and Autonomous Vehicles (CAVs), an emerging mode of transportation, possess the capacity to reduce exhaust emissions greatly. However, accurately measuring carbon emissions in urban transportation remains a challenge, especially considering emissions from electricity generation and gasoline consumption. This paper proposes an innovative method for calculating CAVs' carbon emissions distribution, utilizing both renewable and non-renewable energy. The study employs SUMO, an agent-based simulation platform, to develop an intelligent driver model and cooperative adaptive cruise control modules, tracking vehicle movement behavior across various vehicle types, including Gasoline Vehicles (GVs), Electric Vehicles (EVs), Human-Driven Vehicles (HDVs), and CAVs. Subsequently, a lifecycle electric carbon emission model is constructed, integrating the energy consumption model of EVs with carbon emission factors of renewable and non-renewable energy. Visualization models are then developed to clarify the carbon emission distribution within the traffic network. A case study conducted in Suzhou, China validates the model, analyzing the spatiotemporal distribution of carbon emissions. Results show EVs can reduce carbon emissions by 70%–90 % compared to GVs on urban roads during rush hour, while CAVs can further reduce emissions by 35%–50 % compared to HDVs. Additionally, carbon emissions from non-renewable energy sources were found to exceed those from renewable sources.

In-vehicle nudging for increased Adaptive Cruise Control use: a field study

Close following to lead vehicles is associated with increased risk of rear-end crashes in road traffic. One way to reduce instances of close following is through increased use of the Advanced Driver Assistance System (ADAS) Adaptive Cruise Control (ACC), which is designed to adjust vehicle speed to maintain a safe time headway. Since the activation of ACC is driver-initiated, there is a need to influence the propensity of drivers to use the function. This research aimed to explore whether in-vehicle nudging interventions could be effective for this purpose. A field trial was conducted to consecutively assess the effects of two nudges on drivers’ utilization of ACC, compared to baseline usage. Exposing the participants (n = 49) to the first ambient design nudge resulted in a 46% increase in ACC usage on average. Following the introduction of the second nudge (a competitive leaderboard nudge), the average increase among participants (n = 48) during the complete treatment period reached 61%. The changes in ACC utilization varied between individual drivers, highlighting the need to monitor behavioral outcomes of nudges and adapt them when needed. In conclusion, this research shows that utilizing in-vehicle nudging is a promising approach to increase the use of vehicle functions contributing to improved traffic safety.

Current Approaches in Traffic Lane Detection: a minireview

The continuous development and importance of the field of road transport these days make it necessary to design, develop and implement technological solutions that reduce (eliminate as much as possible) the risk of road accidents. Such a technological solution is also represented by advanced driver assistance systems (ADAS), systems that assist drivers in various ways, such as collision avoidance, automatic parking, adaptive cruise control, attention and lane departure warnings. Over the next ten years, there will likely be a rise in the need for ADAS system deployment in automobile construction, driven by consumer and regulatory interest in safety applications that protect drivers and lower accident rates. At the moment, autonomous emergency braking and forward collision warning systems are mandated for all cars in the US and the EU. Additionally, advanced driver assistance systems (ADAS) may soon distinguish automobile brands and have a significant impact on consumer preference. The present work aims to provide a general picture related to the current research and development of ADAS systems that refer to the detection of the traffic lane and lane markings. The approaches are presented regarding: the current development directions of ADAS systems, current traffic lane detection techniques, traffic lane detection methods and the use of artificial intelligence techniques in this field. The general conclusion is that further research is needed in the field, research to increase the performance of traffic lane detective systems by using advanced algorithms and easy-to-implement methods that do not require large hardware resources.

Efficiency of adaptive cruise control in commercial vehicles

AbstractThe evolution of autonomous vehicles hinges significantly upon the advancements in driving assistance systems. Adaptive cruise control, a pivotal component of these systems, warrants continuous real-world examination to assess its operational efficiency. The study investigates these systems integrated into diverse commercial vehicles with a specific focus on the following distances they provide. The findings reveal that camera-based systems offer shorter following distances relative to ISO standards, while radar-based and combined camera and radar-based systems provide larger following distances. The study contributes to understand adaptive cruise control technology and its alignment with safety standards, thereby aiding in the on-going development of self-driving vehicles.

Incorporating Human–Machine Transition into CACC Platoon Guidance Strategy for Actuator Failure

This study proposes a guidance strategy based on human–machine transition (HMT) for cooperative adaptive cruise control (CACC) truck platoon actuator failures. Existing research on the CACC platoon mainly focuses on upper-level planning and rarely considers platoon planning failures caused by actuator failures. This study proposes that the truck in the platoon creates sufficient space on the target lane through HMT when the actuator fails, thereby promoting lane changes for the entire team. The effectiveness of the proposed strategy is evaluated using the Simulation of Urban Mobility (SUMO) simulation. The results demonstrate that under conditions ensuring the normal operation of traffic flow, this guidance strategy enhances the platoon’s lane-changing capability. In addition, this strategy exhibits stronger robustness and efficiency in different traffic densities. This guidance strategy provides valuable insights into improving the driving efficiency of CACC truck platoons in complex road environments.

Filtering Function to Mitigate the Impact of Cyber Attacks in Cooperative Adaptive Cruise Control

Among automated driving technologies, adaptive cruise control (ACC), which controls the distance between vehicles and their relative speed, is becoming popular. Cooperative ACC (CACC) uses vehicle-to-vehicle communication and sensors to control the distance between vehicles. Recently, cyber attacks against automated driving systems have been a focus, in which information on pedestrians or preceding vehicles obtained from LiDAR and sensors is disguised. Such cyber attacks enable attackers to obtain accurate information on preceding vehicles and pedestrians. This can result in accidents. The purpose of this study is to develop a filtering function to reduce the impact of cyber attacks using available information. The contribution lies in proposing a filtering function for situations in which sensor offsets and communication delays arise owing to cyber attacks.

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.

Cyberattacks on Adaptive Cruise Controls and Emergency Braking Systems: Adversary Models, Impact Assessment, and Countermeasures

Cyberattacks on Adaptive Cruise Controls and Emergency Braking Systems: Adversary Models, Impact Assessment, and Countermeasures

Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity

Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity

Cooperative Adaptive Cruise Control in the Presence of Communication and Radar Stochastic Data Loss

Cooperative Adaptive Cruise Control in the Presence of Communication and Radar Stochastic Data Loss

AI‐Assisted Design of Printed Edge‐Fed Non‐Uniform Zig‐Zag Antenna for mm‐Wave Automotive Radar

AbstractIn this paper, the design of a novel horizontally polarized single‐layer antenna for 77 (GHz) automotive radar applications is4 addressed. An innovative non‐uniform zig‐zag parametrization of the antenna layout is considered to enable a more flexible control on both the impedance matching in the working frequency band and the shaping of the radiated beam pattern with respect to a standard (uniform) one without compromising the linear (horizontal) polarization of the radiated field. Such a polarization guarantees a lower back‐scattering from road pavements, resulting in a reduced amount of clutter and thus allowing a more robust target detection. Moreover, the single‐layer layout has several advantages in terms of fabrication simplicity/costs and mechanical robustness to vibrations. The design of the proposed non‐uniform zig‐zag antenna (NZA) is performed through a customized implementation of the System‐by‐Design (SbD) approach that fruitfully combines machine learning and evolutionary optimization to efficiently deal with the computational complexity at hand. An extensive numerical validation, dealing with designs of different lengths, verifies the high performance of the NZA in terms of beam direction deviation (e.g., BDD < 1 (deg)), sidelobe level (e.g., SLL < −18.2 (dB)), and polarization ratio (e.g., PR > 20 (dB)) within the working frequency band (GHz), as well as its superiority over competitive designs. Finally, the realization of a prototype and its experimental test, validate the proposed NZA concept for automotive mm‐wave radar applications in advanced driver assistance systems and autonomous vehicles such as, for instance, adaptive cruise control, collision avoidance, and blind spot detection.

Age-related differences in takeover performance: A comparative analysis of older and younger drivers in prolonged partially automated driving

Objective Vehicle automation technologies have the potential to address the mobility needs of older adults. However, age-related cognitive declines may pose new challenges for older drivers when they are required to take back or “takeover” control of their automated vehicle. This study aims to explore the impact of age on takeover performance under partially automated driving conditions and the interaction effect between age and voluntary non-driving-related tasks (NDRTs) on takeover performance. Method A total of 42 older drivers (M = 65.5 years, SD = 4.4) and 40 younger drivers (M = 37.2 years, SD = 4.5) participated in this mixed-design driving simulation experiment (between subjects: age [older drivers vs. younger drivers] and NDRT engagement [road monitoring vs. voluntary NDRTs]; within subjects: hazardous event occurrence time [7.5th min vs. 38.5th min]). Results Older drivers exhibited poorer visual exploration performance (i.e., longer fixation point duration and smaller saccade amplitude), lower use of advanced driving assistance systems (ADAS; e.g., lower percentage of time adaptive cruise control activated [ACCA]) and poorer takeover performance (e.g., longer takeover time, larger maximum resulting acceleration, and larger standard deviation of lane position) compared to younger drivers. Furthermore, older drivers were less likely to experience driving drowsiness (e.g., lower percentage of time the eyes are fully closed and Karolinska Sleepiness Scale levels); however, this advantage did not compensate for the differences in takeover performance with younger drivers. Older drivers had lower NDRT engagement (i.e., lower percentage of fixation time on NDRTs), and NDRTs did not significantly affect their drowsiness but impaired takeover performance (e.g., higher collision rate, longer takeover time, and larger maximum resulting acceleration). Conclusions These findings indicate the necessity of addressing the impaired takeover performance due to cognitive decline in older drivers and discourage them from engaging in inappropriate NDRTs, thereby reducing their crash risk during automated driving.

Trajectory Planning for Multiple Autonomous Vehicles at Short‐Distance Tandem Signalized Intersections Based on Rule‐Free Framework

High‐level autonomous vehicles (AVs) have more possibilities for improving traffic efficiency. The improvement of traffic efficiency for mixed flow at near‐saturated short‐distance tandem signalized intersections (STSI) needs attention. Most of the existing studies design a generalized control rule for AVs, ignoring the heterogeneity among different AVs. Herein, a multivehicle trajectory planning framework based on a multiagent reinforcement learning (MRL) algorithm is designed to heuristically explore the optimal traffic efficiency of mixed flow at STSI. The core algorithm of this framework is improved from the classical MRL algorithm multi‐agent proximal policy optimization based on the idea of the virtual group instead of designing control rules. The trajectories planned by the framework show outstanding performance in improving throughputs and reducing emissions at the global system level, comparing natural driving, classic adaptive cruise control model and cooperative adaptive cruise control model. The framework can be used to explore optimal traffic efficiency for mixed flow and better heterogeneous rules for high‐level AVs.

Anti-low angle resolution: Some adaptive improvements in anchor-based object detection algorithm for automotive radar target detection

Automotive radar has extensive and well-established applications in advanced driver assistance systems (ADAS). It has been the most reliable and preferred sensor for functions such as adaptive cruise control and automatic emergency braking. Nevertheless, the limited angle resolution of traditional millimeter-wave radar data reduces its effectiveness in autonomous driving systems (ADS). Our study presents some adaptive improvements in anchor-based object detection algorithm framework to address the inaccuracies in target detection and positioning resulting from the limited angle resolution of millimeter-wave radar data. Our model utilizes the Range-Angle (RA) and Range-Doppler (RD) views of a RAD cube as inputs. An anchor assignment strategy - Max Intersection over Union (IoU) assigner is used to generate more positive samples than before, enabling the model to learn more about the relevant features of targets. We propose a multi-detection box fusion Non-Maximum Suppression (NMS) mechanism to fuse prediction results with varying confidence scores, thus improving the accuracy of target detection positions by addressing the issue of target box position deviation caused by low angle resolution. In addition, an actual distance loss function is designed to constrain the regularity that the size of the bounding box is inversely proportional to the distance between the target and the radar. Based on experimental results, the proposed method increases the mean average precision (mAP) by approximately 10% compared to the baseline method.

A cellular automaton model for mixed traffic flow considering the size of CAV platoon

In the environment of intelligent connected vehicles, the mixed traffic flow consisting of human-driven vehicles (HDVs), connected and automated vehicles (CAVs), and their platoons will coexist for a long time. Exploring and discovering the operational rules of mixed traffic flow is the foundation for its management and control. This study delves into the distinct characteristics of various vehicles within a mixed traffic environment, particularly focusing on HDVs, CAVs, and CAV platoons. Drawing on the psychological predisposition of HDVs to maintain a prudent following distance, we introduce a discrete motion safety distance model to establish the longitudinal movement rule for HDVs. Furthermore, a longitudinal movement rule for CAVs is established based on their ability to keep a constant headway using Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), alongside meeting specific speed and spacing criteria for platoon movement. On this basis, the paper proposes a cellular automaton model for single-lane mixed traffic flow that accounts for CAV platoon length and the decision-time disparity between HDVs and CAVs. The simulation results show that an increased proportion of CAVs can enhance the traffic capacity, elevate average speed, extend average platoon length, and reduce the congestion ratio and CACC degradation rate. Notably, in scenarios where the CAV proportion p > 0.7, the flow-density graph shows a trapezoidal shape, with the majority of CAVs in the traffic flow synchronizing their movements in CACC mode. Even when vehicular density surpasses the optimum threshold, the traffic flow maintains high-speed operation until reaching a critical congestion point, whereupon velocities precipitously decline. Through sensitivity analysis aimed at understanding the impact of maximum platoon length, it has been observed that, for CAV proportions below 0.7, variations in maximum platoon length negligibly impact traffic capacity, velocity, CACC degradation, and congestion levels. Only in scenarios dominated by CAVs does extending platoon length restrictions significantly enhance performance. However, as the maximum platoon length increases, the gains in traffic capacity, speed, CACC degradation rate, and congestion ratio diminish. When the platoon length of CACC exceeds a certain threshold, there will not be a significant improvement in traffic capacity or speed.

Research on a work zone model with metering zone under mixed traffic flow environment

In the upstream of the highway work zone, vehicles will change lanes automatically or forcibly, which has complexity and randomness. Most studies fail to consider the impact of actual mixed traffic characteristics on traffic flow. For example, the dependence of the lane-changing behaviors of Connected and Automated Vehicles (CAVs) on the length of the zone is ignored in upstream of work zone. In addition, it is assumed that vehicles in the merge lane of the work zone would “yield” or not consider an impact on vehicles in the through lane. This simplifies the complex dynamics of the interaction between the two streams of traffic, which leads to the results of the study and the actual situation inconsistency. Based on the Gipps model, the adaptive cruise control (ACC) and the collaborative adaptive cruise control (CACC) vehicle-following models, this paper proposes a continuous cellular automata model under Vehicle-Road Collaboration and studies the mixed traffic flow characteristics in the work zone. By setting a metering zone before the lane reduction sign, ACC/CACC can adjust headway by obtaining road information in advance. The simulation results indicate that ACC/CACC vehicles have better following response compared to human-driven vehicles. In other words, the platoon composed of ACC/CACC vehicles has stability and can improve the traffic capacity of the work zone. The results of the study also found that the insertion of vehicles from adjacent lane into the ACC/CACC vehicles platoon is detrimental, especially when the penetration rate [Formula: see text] is high. Furthermore, when the length of the metering zone [Formula: see text] is set at 120–150[Formula: see text]m, it can significantly improve the road congestion in the work zone.