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This paper presents the ACC system for autonomous trams, including three core processes: object detection, collision assessment and control. The system utilizes the YOLOX deep learning model for camera-based object detection and a clustering approach for LiDAR point clouds, integrating both outputs through a late fusion method for enhanced accuracy. The positions of objects and the tram are projected onto a data map to assess collision risk. This data map, which is a virtual 2D space configured with the track’s UTM coordinates and ROI lines, is used to determine whether objects are located within the ROI. The ACC system adjusts the tram’s speed to control acceleration or deceleration based on the relative distances of objects within the ROI. Experiments on a test track demonstrate effective collision avoidance and reliable ACC system operation under predefined scenarios.

Workload and distraction are problems that affect road driving and can cause very serious accidents. Drive assistance systems are very important to help drivers relieve fatigue of driving by increasing road safety and reducing the number of accidents. The Adaptive Cruise Control (ACC) is a useful driver assistance system to set and maintain a safe distance from the front vehicle, modulating speed and limiting sudden braking. However, the effects of the ACC on overall driver behaviour have not yet been thoroughly investigated. In this study, the authors assessed the influence of ACC on mental workload and attention of drivers in a real driving environment. The results of several evaluations were considered: subjective assessment of workload (NASA-TLX), physiological measurements of workload (brain activity through electroencephalographic technique and the analysis of visual behaviour through an eye monitoring device) and performance-based measurements (via Vbox Pro Video mounted on the vehicle). 52 drivers were involved in the study, 26 ACC experts and 26 non-experts, who drove along the ring road of Bologna (Tangenziale di Bologna) (Italy), both using ACC and manually, without the help of any device. During the test a secondary task was introduced: the sudden arrival of a braking car. Results showed that the use of ACC increased distraction when driving.

A substantial number of vehicles nowadays are equipped with adaptive cruise control (ACC), which adjusts the vehicle speed automatically. However, experiments have found that commercial ACC systems which only detect the direct leader amplify the propagating disturbances in the platoon. This can cause severe traffic congestion when the number of ACC-equipped vehicles increases. Therefore, an ACC system which also considers the second leader further downstream is required. Such a system enables the vehicle to achieve multi-anticipation and hence ensure better platoon stability. Nevertheless, measurements collected from the second leader may be comparatively inaccurate given the limitations of current state-of-the-art sensor technology. This study adopts deep reinforcement learning to develop ACC controllers that besides the input from the first leader exploits the additional information obtained from the second leader, albeit noisy. The simulation experiment demonstrates that even under the influence of noisy measurements, the multi-leader ACC platoon shows smaller disturbance and jerk amplitudes than the one-leader ACC platoon, indicating improved string stability and ride comfort. Practical takeaways are twofold: first, the proposed method can be used to further develop multi-leader ACC systems. Second, even noisy data from the second leader can help stabilize traffic, which makes such systems viable in practice.

This paper discusses the Adaptive Cruise Control (ACC) system, which is a solution that allows drivers to minimize the amount of time spent driving. It supports four different driving modes and regulates the acceleration and deceleration of the car in order to maintain a fixed speed or avoid a collision with another vehicle. Real-Time Publish-Subscribe (RTPS) middleware has emerged as one of the most efficient and practical options for the real solutions to the difficulties listed above. This paper proposes to develop a real-time system for integrating the various components of ACC, such as Information Cluster, Radar, Brake Switches, Cruise Switches, ACC Controller, Engine/Throttle Controller, Brake Controller, Brake Actuators, Speed Sensors, and Back Brake Lights. The exchange of data is through a RTPS Data Distribution Service (DDS) middleware. The design of the publish/subscribe model was explained in detail in this paper along with the proper Quality of Service (QoS) policies suggested to govern the behavior of the model.

In this paper, we present an advanced adaptive cruise control (ACC) concept powered by Deep Reinforcement Learning (DRL) that generates safe, human-like, and comfortable car-following policies. Unlike the current trend in developing DRL-based ACC systems, we propose defining the action space of the DRL agent with discrete actions rather than continuous ones, since human drivers never set the throttle/brake pedal level to be actuated, but rather the required change of the current pedal levels. Through this human-like throttle-brake manipulation representation, we also define explicit actions for holding (keeping the last action) and coasting (no action), which are usually omitted as actions in ACC systems. Moreover, based on the investigation of a real-world driving dataset, we cast a novel reward function that is easy to interpret and personalized. The proposed reward enforces the agent to learn stable and safe actions, while also encouraging the holding and coasting actions, just like a human driver would. The proposed discrete action DRL agent is trained with action masking, and the reward terms are completely derived from the real-world dataset collected from a human driver. We present exhaustive comparative results to show the advantages of the proposed DRL approach in both simulation and scenarios extracted from real-world driving. We clearly show that the proposed policy imitates human driving significantly better and handles complex driving situations, such as cut-ins and cut-outs, implicitly, in comparison with a DRL agent trained with a widely-used reward function proposed for ACC, a model predictive control structure, and traditional car-following approaches.

Transportation sector and its impacts on climate change have received much attention over the last decade. Energy-optimal vehicle control algorithms such as adaptive cruise control can potentially reduce fuel consumption and the consequential environmental impacts. Adaptive cruise control algorithms optimize vehicles’ speed to lower energy consumption considering several constraints, including safety, stability, and comfortability. A wide range of algorithms with different objectives and optimization mechanisms have been reported in the literature. Due to the high diversity of constraints and objectives, a comprehensive study of these algorithms is required. In recent literature reviews, there is no comprehensive summary of the adaptive cruise control algorithms’ features, classes, and objectives. This paper presents a holistic literature review of energy-optimal adaptive cruise control algorithms. We gathered relevant publications from well-eminent journals. Information on diversity and the number of publications will be presented to provide data on used references. For each paper, the objectives, features, and relevant classes of algorithms were extracted. Then a classification based on objectives is suggested, and mathematical formulas of representative studies are summarized to provide the required knowledge regarding mathematical modeling and optimization in this field. The study provides a useful insight into the development of the cruise control systems research field, revealing those scientific actors (i.e., authors, developers, and institutions) that have made the biggest research contribution to its development.

Domain-based electronic and electrical (E/E) architectures have been regarded as a possible upgrade to distributed E/E architectures currently used in electric vehicles. In a distributed E/E design, E/E components are directly connected to the automobile bus. Domain-based architectures split E/E components into distinct domains depending on their functions, which clearly benefits software upgrading and wire harness reduction. However, due to its heterogeneous topology with multiple network protocols, domain-based E/E architecture introduces complicated multilink and multinode delays into the control loop. The delays may degrade and even deteriorate the stability of adaptive cruise control (ACC) employing domain-based E/E architecture. To this end, this article proposes a heterogeneous-topology loop delay analysis by introducing a notion of poly-service loop delay. With a graphical pattern, the analytical process is presented in depth. The worst-case loop delay is calculated using an upper-boundary mathematic equation. Then, a hierarchical cyber-physical control method for ACC is designed. The upper level is intended to achieve desired acceleration based on vehicle and intervehicle motion states. And the lower level is intended to mitigate the negative impact of loop delays and provide reliable acceleration tracking. The results of cosimulation and hardware-in-loop experiment verify effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]

In this article, we propose an ecological predictive cruise control method for connected and automated vehicles (CAVs) using data-driven predicted leading vehicle speed in a car-following scenario. Many existing studies assume that the leading vehicle's behavior is known when planning the trajectory of the ego vehicle. Unfortunately, predicting the future behavior of adjacent vehicles is extremely uncertain and inaccurate for use in control. To overcome this, we adopt the vector autoregressive model (VAR) that is well suited for generating simultaneous forecasts of the response variables when predicting the short-term behavior of the vehicle ahead. As many human drivers behave similarly in a car-following situation, vehicle connectivity is specifically used to drop hints of the behaviors of the cars following the connected car. Once the leading vehicle's future trajectory is predicted, the ego vehicle is controlled in a way that minimizes energy consumption by optimizing its speed trajectory while remaining safe. Through simulation case studies, we demonstrate that our approach can achieve improved energy efficiency considerably than the conventional strategy that cannot predict the speed of vehicles ahead. Given the recent market penetration of vehicle connectivity technologies and advanced driving assistance systems (ADAS), the proposed method is expected to have a high commercialization potential. [ABSTRACT FROM AUTHOR]

Most existing studies modeled Adaptive Cruise Control (ACC) car-following (CF) behavior using conventional CF models which were originally built for human-driving vehicles (HVs) and calibrated with HV data. In this paper, firstly a learnable CF model is proposed by resorting to Long Short-Term Memory (LSTM) for ACC systems, which utilizes the ACC data for model construction and offers extraordinary adaptability and accuracy. Nevertheless, the applicability of the LSTM CF model is hindered by the scarce ACC data problem, as training the model requires a large amount of data. To address the ACC data scarcity problem, a transfer learning strategy for the LSTM model is further developed, leveraging on the large-scale open-source HV data and the similar driving patterns hidden in HV and ACC-equipped vehicles. In the transfer learning based LSTM model, a unified framework incorporating an alignment layer is developed to transfer the useful features from HV data and meanwhile calibrating the CF model with ACC data. Comparison results show that the proposed model outperforms other CF models which are built with only ACC data or using simple transfer learning methods. Further, microscopic simulations are performed to verify the applicability of the transfer learning based LSTM CF model. [ABSTRACT FROM AUTHOR]

With the development of vehicle electrification, automation and connectivity, collaborative optimization among the traffic throughput, driving comfort, fuel economy and driving safety targets is still a huge challenging barrier for a connected and automated fuel cell/battery hybrid electric vehicle. Hence, this paper proposes an optimal car-following energy management strategy (EMS) that combines energy management and adaptive cruise control considering the above targets. Specifically, based on vehicle-to-vehicle and vehicle-to-infrastructure information, an optimal following distance algorithm is developed to obtain the optimal following distance considering driving safety, driving comfort and traffic throughput. Then, based on the established vehicle longitudinal dynamics model, an adaptive cruise controller using back-stepping technique is designed to accurately track optimal following distance. Meantime, combining the obtained controller, optimal EMS based on equivalent consumption minimization strategy is proposed to coordinate the output power of fuel cell and battery to improve fuel economy. The simulations of short and long-term driving cycles indicate that the proposed method can reduce hydrogen consumption by 12.12%, jerk by 61.21%, and keep the desired following distance tracking error within 0.5m. [ABSTRACT FROM AUTHOR]

Advanced driver-assistance systems (ADAS) have matured over the past few decades with the dedication to enhance user experience and gain a wider market penetration. However, personalization features, as an approach to make the current technologies more acceptable and trustworthy for users, have been gaining momentum only very recently. In this work, we aim to learn personalized longitudinal driving behaviors via a Gaussian Process (GP) model. The proposed method learns from individual driver’s naturalistic car-following behavior, and outputs a desired acceleration profile that suits the driver’s preference. The learned model, together with a predictive safety filter that prevents rear-end collision, is used as a personalized adaptive cruise control (PACC) system. Numerical experiments show that GP-based PACC (GP-PACC) can almost exactly reproduce the driving styles of an intelligent driver model. Additionally, GP-PACC is further validated by human-in-the-loop experiments on the Unity game engine-based driving simulator. Trips driven by GP-PACC and two other baseline ACC algorithms with driver override rates are recorded and compared. Results show that on average, GP-PACC reduces the human override duration by 60% and 85% as compared to two widely-used ACC models, respectively, which shows the great potential of GP-PACC in improving driving comfort and overall user experience. [ABSTRACT FROM AUTHOR]

This paper analyzes trajectory observations from vehicles driving in platoon formation and they are equipped with Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) systems; aiming to quantify response delays. When a preceding vehicle induces a perturbation, the delay until the reaction of the following vehicle, often quoted as observable response time, can have negative implication to the traffic flow and other factors such as energy consumption, stability and safety. Quantifying such delays can help towards more realistic traffic simulation modeling. The analysis is performed based on empirical observations from three well-known experimental campaigns in the literature with data from ACC-driven and CACC-driven vehicle platoons. Three state-of-the-art techniques were implemented to provide quantitative results for the observed response times. The benefits and downsides of each technique are discussed as well. The results show that ACC systems do not exhibit a significant improvement compared to human drivers, yet, it can be concluded that the additional vehicle-to-vehicle communication incorporated in the CACC systems allows for a substantially higher traffic flow and possibly other benefits. [ABSTRACT FROM AUTHOR]

This paper solves the problem of designing local adaptive cruise controllers that guarantee collision avoidance of the vehicles in a platoon. Whereas previous studies have solved this problem for specific vehicle models, the present paper shows a general solution for a wider class of linear vehicle models. In particular, the problem of finding a controller that renders a vehicle externally positive to guarantee string stability and collision avoidance is studied in detail. The result is an algorithm that finds a set of closed-loop eigenvalues that correspond to an externally positive feedback loop. The developed methods are applied to an example and the effectiveness is verified in a laboratory experiment with multiple robots driving in a platoon. [ABSTRACT FROM AUTHOR]

Road geometry (e.g. slope and curvature) has significant impacts on driving behaviours of low-level automated vehicles (AVs), but it has been largely ignored in microscopic traffic models. To capture these effects, this study proposes a generic approach to extend any (free-flow or car-following) microscopic models characterized by acceleration functions. To this end, three model extensions are developed, each of which can use different submodels for comparison. Their effectiveness is demonstrated with a microscopic free-flow model that represents the AVs’ control logic. Finally, all possible combinations of the microscopic model and the model extensions are calibrated and cross-validated against data sets, which contain empirical trajectories of commercial adaptive cruise control (ACC) systems on different test tracks. Results suggest that two submodels, i.e., the nonlinear vehicle dynamics (NVD) and the radius difference method (RDM), can extend the microscopic model to effectively capture the effects of road slope and road curvature, respectively, on automated driving. Specifically, the NVD is the dominant factor contributing to increasing (by 34.9% on average) model accuracy. In addition, when simulating reckless turning behaviours (i.e. the vehicle turns at high speeds), the inclusion of the developed RDM is significant for model performance, and the models extended with both the NVD and the RDM can achieve the largest accuracy gains (39.6%). [ABSTRACT FROM AUTHOR]

Intelligent electric vehicles (IEVs) have attracted more and more attention benefitting from the characteristics of high degree of safety and energy efficiency. This paper proposes an adaptive cruise control framework considering regenerative braking to improve safety and energy efficiency of IEVs during the car-following process. At first, a coupled and nonlinear dynamic model of IEVs system is constructed, which is mainly composed of a powerful battery, an electric motor, a single-speed transmission, and a hydraulic braking system. Then, an adaptive fuzzy sliding mode high-level controller is designed to accurately obtain the desired longitudinal acceleration of IEVs, in which the fuzzy logic is utilized to approximate the switching control item of the sliding mode control for chattering free. And the stability of high-level controller is proven by the Lyapunov theory. In the lower-level controller, traction control and brake control are designed to track the desired acceleration produced by the high-level controller, in addition, a novel regenerative braking strategy is presented to maximize the braking energy recovery. Finally, the simulation results indicate that the proposed control scheme has the excellent performance of longitudinal tracking and braking energy recovery with no loss of safety. [ABSTRACT FROM AUTHOR]

Trust is a crucial aspect for autonomous vehicles (AVs) and automated driving systems (ADSs) that maintains humans’ acceptance of such functions. However, trust dynamic models describing the human-vehicle relationship rarely exist. To this end, this paper develops a quantitative trust dynamic model of the driver to an adaptive cruise control (ACC) system and applies the proposed model to a trust-based ACC. Driver’s trust level is modeled as an objective evaluation index for assessing the individual perceived trustworthiness on automation. Three contributions have been made in this work: 1) a novel quantitative dynamic model describing the driver’s trust on ACC is proposed for the first time considering the effects of the driver’s taking-over and handing-over operations as well as the steady-state driving scenarios; 2) an improved control barrier function approach with guaranteed system stability and prescribed asymptotic performance is proposed to design the trusted-based ACC, satisfying the state, input, and safety constraints; and 3) a new prescribed performance function that does not require accurate value of the initial condition is proposed to restrict the tracking errors within the predefined asymptotic boundaries, given that the initial value of the trust is hard to obtain. High-fidelity CarSim-based simulations demonstrate the rationality of the proposed trust model and the validity of the proposed control approach. [ABSTRACT FROM AUTHOR]

Today, advanced driver-assistance systems (ADAS) come up with different abilities. One of them is the adaptive cruise control (ACC) system. The ACC system is a continuation of research on cruise control (CC) system, which integrates spacing control with the existing velocity control on the CC system. The vehicles with an ACC system guarantee traffic safety while at the same time ensure a well-driving sense. Many studies have demonstrated numerous control techniques applied as ACC controllers to accomplish uncertainty and perturbation issues. Nevertheless, most of the existing papers assumed the model vehicle dynamics as a linear time-invariant (LTI) system while designing the ACC controller. This paper proposed an ACC controller using the gain scheduling technique to deal with the model vehicle dynamics as a linear parameter varying (LPV) system. The passenger vehicle’s mass varies during ACC operation depending on how many passengers or loads on the vehicle’s trunks. Later, the vehicle’s mass is estimated by recursive least square (RLS) with a forgetting factor. Then, the disk margin is utilized to provide the high-level robustness at each operating or “frozen” point. The robustness performance will be analyzed using the worst-case gain metric while the uncertainty is modeled by integral quadratic constraints (IQC). The LPV system behavior, such as the rate vehicle’s mass, is also considered in the analysis. The effectiveness algorithm is validated through joint simulation between Matlab/Simulink and PreScan. The last, the comparison performance between gain scheduling and fixed gain ACC controller is evaluated.

This paper proposes a defensive ecological adaptive cruise control (DEco-ACC) algorithm that is capable of reducing an ego vehicle’s dwelling time in the blind spot zones (BSZs) of its neighboring vehicles. To this end, a model predictive control is applied in the use of information such as speed, position, and blind spot zones about preceding and neighboring vehicles. The cost function of the DEco-ACC consists of tracking performance, control effort, and dwelling time in BSZs. Specifically, a continuous and one-time differentiable penalty function is introduced to handle the constraints regarding the BSZs. For optimizing and evaluating the performance of the proposed DEco-ACC, real-world traffic data from Next Generation Simulation (NGSIM) are used to analyze and generate car-following scenarios during highway driving. Especially, in consideration of the most probable case that one neighboring vehicle exists at one adjacent lane, a parametric study is conducted to investigate the impact of the weighting factors on the performance of the DEco-ACC. The simulation results from 100 cases demonstrate that on average, the DEco-ACC with optimized weighting factors can reduce the dwelling time in the neighboring vehicles’ BSZs by 46.3% without significant deterioration of fuel consumption (0.04% increase in average fuel consumption) and drivability, as compared to the Eco-ACC, whose primary objective is the minimization of fuel consumption during safe car-following.

Recently developed efficient driving schemes usually solve a predictive optimization problem or determining the vehicle control input, and at the expense of high computational cost, they improve the overall traffic flows and individual driving performances on urban roads. This paper presents a more practical technique for automated vehicles’ predictive driving by extending the existing adaptive cruise control (ACC) scheme with a look-ahead functionality. Such a look-ahead driving scheme (LDS) predicts the states of the preceding vehicle at an adaptive look-ahead time step and, with negligible computation costs, computes the vehicle control input more circumspectly for efficient driving in urban traffic. The proposed LDS is evaluated in typical urban traffic at the signalized intersections by observing the intersection utilization, flowing characteristics, and individual vehicles’ fuel efficiency. Furthermore, we also evaluate the influences of the LDS-vehicles’ penetration rates on overall traffic performances at various traffic volumes. [ABSTRACT FROM AUTHOR]

In this article, we assess the string stability of seven 2018 model year adaptive cruise control (ACC) equipped vehicles that are widely available in the US market. Seven distinct vehicle models from two different vehicle makes are analyzed using data collected from more than 1,200 miles of driving in car-following experiments with ACC engaged by the follower vehicle. The resulting dataset is used to identify the parameters of a linear second order delay differential equation model that approximates the behavior of the black box ACC systems. The string stability of the data-fitted model associated with each vehicle is assessed, and the main finding is that all seven vehicle models have string unstable ACC systems. For one commonly available vehicle model that offers ACC as a standard feature on all trim levels, we validate the string stability finding with a multi-vehicle homogeneousplatoon experiment in which all vehicles are the same year, make, and model. In this test, an initial disturbance of 6 mph is amplified to a 25 mph disturbance, at which point the last vehicle in the platoon is observed to disengage the ACC. The data collected in the driving experiments is made available, representing the largest publicly available comparative driving dataset on ACC equipped vehicles. [ABSTRACT FROM AUTHOR]

This study aims to develop an optimal cruise controller that can automatically adapt to individual car-following style. First, the adaptive cruise control (ACC) problem is formulated as a linear quadratic optimal control, and an optimal control law containing the longitudinal acceleration of the target vehicle is derived. Then, a certain number of individual car-following styles are predefined on the basis of the proposed optimal cruise controller. Thereafter, a car-following style learning algorithm is proposed to quantify the closeness of the predefined individual car-following style to the specific driver, and a proper style is thus determined for the specific driver by using this learning algorithm. On the basis of the learned car-following style, the proposed optimal cruise controller can adapt itself to individual car-following style. Finally, the proposed self-learning optimal cruise controller is evaluated through simulation and experimental tests. Results show that the control behavior of the proposed self-learning optimal controller is closer to that of the human driver than that of a factory-installed ACC. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) is utilized to provide route guidance information to land and autonomous vehicles. The GNSS–based positioning and navigation (POS/NAV) usually suffer from satellite signal blockage, interference, and multipath in urban areas. Autonomous land vehicles are equipped with cameras, radars, and laser ranging devices. The availability of these systems provides an attractive opportunity to increase the POS/NAV system accuracy. This research focuses on the development of an integrated multi-sensor POS/NAV system capable of offering seamless positioning for autonomous land vehicles. A new multi-sensor POS/NAV module integrating both adaptive cruise control frequency modulated continuous wave (ACC-FMCW) radar (RAD), and magnetometer measurements with the reduced inertial sensor system (RISS) was designed to update the navigation system during GNSS outages. Augmenting RAD/RISS system with the magnetometer measurements produces a robust solution. The designed system is further improved by utilizing fast orthogonal search (FOS) to provide nonlinear error modeling of the residual errors associated with the RAD/RISS positioning solution in order to reduce the error growth overextended and frequent GNSS outages.The proposed systems were evaluated on several real road test trajectories involving different types of land vehicles experiencing different motion dynamics. GNSS outages of up to 10 minutes were intentionally introduced to examine the performance. The results show that the proposed methods have resulted in a significant performance improvement in the positioning accuracy that can reach more than 80% if compared to the present techniques that rely only on integrating the inertial sensor technology with GNSS. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance for drivers to enhance safety and reduce workload. As the core of all ACC control algorithms, the spacing policy plays a crucial role in various aspects. This paper presents a comprehensive survey on spacing policies for existing ACC solutions in the literature. The objectives of this paper are to clarify the operating mechanisms and characteristics of the common spacing policies, and to reveal their advantages and shortcomings by means of a comparative study. In this survey, the general evaluation criteria for spacing policies are first introduced. Then, the existing spacing policies are categorized into different types according to their operating mechanisms, and their characteristics are carefully reviewed and explained. A comparative study is followed to analyze the performances of five typical spacing policies in the literature, including the constant spacing policy, constant time headway, traffic flow stability, constant safety factor and human driving behavior spacing policies. The contents provided in this paper serve as a tool for understanding current ACC spacing policies, and pave the way for future ACC enhancement.

The decision-making system of intelligent vehicles is the core component of an advanced driving system for both passenger vehicles and commercial vehicles. Finding ways to improve decision-making strategies to suit the complex and unfamiliar environments is a standing problem for traditional rule-based methods. This paper proposes a semi-rule-based decision-making strategy for heavy intelligent vehicles based on the Deep Deterministic Policy Gradient algorithm. Firstly, according to the car-following characteristics, the problems of high dimensions and a large amount of data in vehicle action space and state space are solved by dimension reduction and interval reduction to accelerate the training process. Subsequently, an accurate three-axle vehicle load model is established to calculate the load transfer rate value and carry out active control to increase the roll stability of heavy vehicles at high-speed corners. Furthermore, the Deep Deterministic Policy Gradient algorithm is developed based on the reward function and update function to achieve adaptive cruise control objectives for heavy vehicles on different curvature roads. Finally, the effectiveness and robustness of the algorithm are verified through simulation experiments.

Vehicle-borne millimeter wave radar sensor may malfunction during signal acquisition and transmission process, which will affect the decision of adaptive cruise control (ACC) system and the safe driving of vehicles. However, adding other redundant environment-aware devices will increases costs. Therefore, in this paper, an active fault tolerant control of ACC system considering vehicle-borne millimeter wave radar sensor failure is proposed. Sensor faults are taken as discrete events, and the mixed logical dynamical (MLD) model of ACC upper control system is built which includes both the fault-free dynamics and the fault dynamics of the system. Then, the active fault tolerant control model of ACC system is established based on model predictive control (MPC) framework. Compared with the existing researches, this work emphasizes on the active fault tolerant control without adding other redundant environment-aware devices, which has not been fully revealed by the existing researches and is important to the industrial application. Moreover, the active fault tolerant control method can be easily ported to other driver assistance system besides ACC. The simulation results show that the vehicle equipped with the active fault tolerant ACC system can still drive safely and smoothly without being affected by radar sensor failures, which demonstrates its great significance to improve vehicle's own intelligence and ensure the safe driving instead of relying entirely on sensors.

Recent years, advanced driver assistance system (ADAS) has been highly developed and widely used. Among various ADAS techniques adaptive cruise control (ACC) is the fundamental of longitudinal control. The tradeoffs among safety, comfort and traffic efficiency are the main issue in ACC design process. In this study, large amount of road tests was carried out to explore the circumstance characteristics in ACC usages scenarios. Based on statistical analysis of road test data, a method for ACC safety performance evaluation is proposed. Furthermore, the effects of parameters such as following distance, jerk limit and time delay to ACC safety performance are discussed. Finally, a way of ACC parameter designing is developed, this method can obtain best comfort and traffic efficiency after safety request is guaranteed. In this study, regulations and realistic constrains of production vehicles are considered. Different from theoretical simulation, production-oriented ACC system is restricted in maximum deceleration, maximum braking jerk, detection latency, actuation latency, calculation capacity etc. For this reason, all the analysis is based on statistical results and requests from actual products, no computationally expensive optimal method is implemented. The proposed method may not get optimal results in theoretical simulation but is very instructive in production-oriented ACC system design.

During the car following process, in order to improve ACC ability to coordinate various goals and adaptability in a complex and variable driving environment, the variable time headway spacing strategy and multi-objective adaptive cruise control algorithm are studied. Aiming at the deceleration adaptability, an improved variable time headway strategy is proposed, which adjusts the value of acceleration weight according to the deceleration duration and deceleration changes to increase the relative distance between the two vehicles in deceleration conditions. In order to improve the multi-objective coordination ability, the longitudinal kinematics model between the two vehicles is established, the objective function and constraints considering multiple factors are designed, and the relaxation factor vector is introduced to soften the boundary of different hard constraints to solve the problem with no feasible solution. Based on model predictive control theory, the objective function is converted into a multi-constrained quadratic programming problem in the rolling optimization process, and an improved particle swarm algorithm is used to solve the function. Through numerical simulation, the results show that the improved ACC algorithm can effectively improve the safety of the vehicle during deceleration, as well as the fuel economy, comfort and tracking ability under cycling conditions. The effectiveness of the algorithm is verified by the joint simulation of MATLAB/Simulink and CarSim.

This paper presents a personalized stochastic optimal adaptive cruise control (ACC) algorithm for automated vehicles (AVs) incorporating human drivers' risk-sensitivity under system and measurement uncertainties. The proposed controller is designed as a linear exponential-of-quadratic Gaussian (LEQG) problem, which utilizes the stochastic optimal control mechanism to feedback the deviation from the design car-following target. With the risk-sensitive parameter embedded in LEQG, the proposed method has the capability to characterize risk preference heterogeneity of each AV against uncertainties according to each human drivers' preference. Further, the established control theory can achieve both expensive control mode and non-expensive control mode via changing the weighting matrix of the cost function in LEQG to reveal different treatments on input. Simulation tests validate the proposed approach can characterize different driving behaviors and its effectiveness in terms of reducing the deviation from equilibrium state. The ability to produce different trajectories and generate smooth control of the proposed algorithm is also verified.

Numerous automotive control systems depend on the prediction of the velocity of the controlled vehicle or other road users in close proximity. A forecast is indispensable for the design and operation of model predictive control strategies which are utilized for example in order to optimize fuel economy in cruise control systems. In this article, a novel velocity time series prediction method, which depends exclusively on past velocity measurements, is presented and analyzed. It is based on conditional probabilities of acceleration and deceleration, which are estimated from real driving data. The proposed velocity prediction is used in a new model predictive control algorithm for an adaptive cruise control system for heavy duty vehicles, where the predicted future velocity of a preceding vehicle is needed, as well as its probability distribution. Simulation results show that an accurate velocity prediction is crucial for achieving energy-efficiency goals. [ABSTRACT FROM AUTHOR]

The influence of (cooperative) adaptive cruise control (CACC/ACC) on rear-end crashes have been conducted by some previous studies. However, they usually did not consider the degeneration from stable CACC to unstable ACC when vehicle-to-vehicle (V2V) communication is not available. California PATH program validated the instability of ACC system by using real experimental data, which might have negative impacts on safety. Therefore, this paper evaluated the impacts of degeneration of CACC vehicles on rear-end crash risks. A micro-simulation based method was adopted to evaluate rear-end crash risks with the increase of the CACC market penetration rate (MPR). The results obtained were quite different from those in previous studies and showed that the rear-end crash risks of the full MDV flow increase before CACC MPR increases to a certain level. It was found that the instability of ACC vehicles might be the main factor for the negative results. The results of this paper provide helpful insight into the management of CACC vehicular flow in the V2V communication environment.

While anecdotal evidence suggests the consideration of a constant time-to-collision (TTC) threshold in advanced driver assistance systems, yet the prospect of the warning criterion acting as a catalyst for the evaluation of conflict severities in non-lane based traffic environments needs further assessment. The current study attempts to provide a copula-based framework to capture the potential dependence between TTC and centerline separation between the interacting vehicles in staggered-following events according to lead vehicle type. Detailed trajectory data extracted from the video footage of urban roads were used for the purpose of this study. Considering negative association between the considered variables and checked by the performance measures, the bivariate Frank copula model is found acceptable to model the bivariate dependency structure between TTC and CS. In particular, the results of the study corroborate a pragmatic decreasing relationship of TTC with increasing CS and decrease in lead vehicle size- a finding which indicates that the risk level of riders in staggered-following scenario varies by the type of front vehicle ahead and the lateral positioning of the vehicles. Based on the results presented in this paper, our recommendation is to use different minimum TTC thresholds for different centerline separations and leader-follower pairs to suitably evaluate traffic safety in such non-lane based traffic environments. [ABSTRACT FROM AUTHOR]

This paper proposes three route control strategies for autonomous vehicles (AVs) exiting to off-ramps. First, two microscopic lane-changing models are developed to simulate the off-ramp behaviors for AVs. A risk factor $\lambda $ is introduced in the discretionary lane-changing (DLC) model, and a five-step process is developed for the mandatory lane-changing (MLC) model. Second, three route control strategies are presented, i.e., grading strategy (GS), half-overlap strategy (HOS), and full-overlap strategy (FOS). Finally, the extensive numerical simulations are conducted to analyze the characteristics of each strategy based on the MATLAB platform. The results indicate that the studied area displays significant variability in spatiotemporal dynamics of average velocity and road capacity under different strategies. It is found that the GS is the most effective control strategy for traffic congestion dissipation and travel delay reduction, followed by the HOS and, then, the FOS. The road capacity under the GS is increased by 19.1% and 7.8% compared to the FOS and the HOS, respectively. Furthermore, the GS consumes lower costs in terms of safety, travel efficiency, and route and lane switch frequency compared to the HOS and FOS, which is verified as the optimal strategy according to overall running cost. The findings of this paper can provide useful references for the design of AVs as well as the management of the automated highway systems. [ABSTRACT FROM AUTHOR]

Road vehicles are characterized by increasing levels of automation and it is vital to understand the future impact on transport efficiency. Adaptive Cruise Control (ACC) is one of the first and most common automated functionalities available in privately owned vehicles. The effect of ACC on traffic flow has been widely studied by making assumptions on its operating strategy and on some of its important parameters such as the response time and the desired time headway. In the literature, these parameters are usually set to low values, based on the vehicle controller’s theoretical ability to respond within a very short time frame. Response time is known to be an important parameter in defining the capacity of the road and therefore, assuming a very short response time, studies usually conclude that systems like the ACC will contribute increasing the road capacity significantly. The present study aims at measuring the actual response time of an ACC-enabled vehicle in car-following conditions. A new methodology for the estimation of the controller’s response time and the desired time-gap was developed to this objective. Results show that the response time of the particular ACC controller was in the range 0.8s-1.2s, which is similar to what is commonly assumed for human drivers. In this light, the results of the present study question the common assumption that ACC or other automation technologies necessarily improve traffic flow and increase road capacity. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of modified cooperative adaptive cruise control to support high-density car platooning. We first use a simplistic communication model to study the impact of actuation lag, message periodicity, and communication delay on the minimum feasible inter-car spacing. We then use a detailed IEEE 802.11p simulation model to evaluate platooning performance in realistic highway scenarios. Different highway traffic intensities are simulated to observe the impact of increasing contention on the wireless channel with two different transceiver configurations: a single-transceiver operating on the common safety channel and a dual-transceiver operating, simultaneously, on the common safety channel and a dedicated service channel. [ABSTRACT FROM AUTHOR]

In this paper, an ecological adaptive cruise controller (ECO-ACC) for parallel hybrid electric vehicles (HEVs) in a car-following scenario is presented to improve the fuel economy and to maintain a desired inter-vehicle distance from the preceding vehicle. An ACC based on action dependent heuristic dynamic programming (ADHDP) is proposed to obtain an ecological velocity profile and realize an active distance control in normal driving situations. ADHDP is able to adapt internal parameters online and can thus deal with systems with disturbances. Furthermore, an adaptive energy management strategy for HEVs is introduced to control the gear shift and power split for fuel consumption optimization. The gear shift command is designed by enumeration, and the power distribution between the engine and the electric motor is performed by ADHDP. The developed ACC and energy management strategy are finally combined to an ECO-ACC to achieve a multi-objective optimization. Only the current velocity and acceleration of the preceding vehicle are used while knowledge about the future velocity is not needed. The simulations of different driving cycles indicate that the ECO-ACC can lead to near-optimal fuel economy and comfortable driving. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC) is one of key technologies of advanced driver assistance systems. Challenges still need to be solved to improve the performance of ACC, such as ensuring vehicle dynamic stability during car following processes on curved roads, and driving comfort. Therefore, a multiple-objective ACC (MOACC) integrated with direct yaw moment control (DYC) is proposed to ensure vehicle dynamic stability and improve driving comfort on the premise of car following performance, and its hierarchical structure consists of an upper layer and a lower layer. First, the upper layer is designed based on model predictive control (MPC). It coordinates ACC and DYC utilizing vehicle dynamics states, and tire forces of four wheels are calculated by the MPC upper layer. Then, the lower controller implements desired tire forces by controlling the throttle and brake pressure. A slip ratio controller is used in the brake pressure calculation module, and the electronic throttle opening calculation module is based on the look-up table method. Finally, the proposed MOACC is tested by a platform simulation. Results illustrate that the proposed controller performs better than the traditional ACC. [ABSTRACT FROM AUTHOR]

In this paper, we propose a vehicle path prediction employing yaw acceleration for adaptive cruise control (ACC). First, a path prediction method employing yaw acceleration is proposed to improve the path prediction performance of ego vehicles. In the proposed method, the vehicle path is predicted by using a clothoidal cubic polynomial curve model, and for this purpose, the curvature rate, yaw rate, and longitudinal velocity are required. The curvature rate can be mathematically obtained by differentiating the yaw rate without a camera sensor. To obtain the yaw acceleration from the noisy measured yaw rate, we derive the state-space model from the steer wheel angle for the yaw rate. Then, the KF is designed by using the state-space model to estimate the yaw acceleration. Second, a multirate longitudinal control method is proposed to improve the longitudinal control performance. The multirate KF employs the constant acceleration model in order to estimate the relative distance and velocity of the target vehicle at a faster sampling rate. Then, the desired acceleration is achieved to maintain a safe headway distance or velocity by means of the longitudinal controller. Consequently, the whole ACC system operates with a faster sampling rate so that multirate control scheme reduces ripples in both the relative longitudinal distance and the desired acceleration. The performance of the proposed method was evaluated via simulations and experiments. [ABSTRACT FROM AUTHOR]

We develop a predictor-based adaptive cruise control design with integral action (based on a nominal constant time-headway policy) for the compensation of large actuator and sensor delays in vehicular systems utilizing measurements of the relative spacing as well as of the speed and the short-term history of the desired acceleration of the ego vehicle. By employing an input–output approach, we show that the predictor-based adaptive cruise control law with integral action guarantees all of the four typical performance specifications of adaptive cruise control designs, namely, 1) stability, 2) zero steady-state spacing error, 3) string stability, and 4) non-negative impulse response, despite the large input delay. The effectiveness of the developed control design is shown in simulation considering various performance metrics. [ABSTRACT FROM AUTHOR]

Autonomous driving technologies can provide greater safety, comfort, and efficiency for future transportation systems. Until now, much of the research effort has been devoted to developing different sensing and control algorithms. However, there has been limited research on how to handle sensor errors efficiently. A simple error in the sensor may lead to an unexpected failure in the whole autonomous driving function. In those cases, the vehicle is then recommended to be sent back to the manufacturer for repair, which costs time and money. This paper introduces an efficient automatic online sensor correction method. The method includes four major functions: sensor error detection, human teaching, vehicle learning, and vehicle self-evaluation. The first function is assumed to be ready and the major contribution of this paper is the human-vehicle teaching and learning framework, which utilizes human-vehicle interaction to collaboratively adjust the parameter in the control model in order to compensate for the errors of the sensors. The self-evaluation function is also briefly introduced. The applications of this method to radar and vision sensors to recover adaptive cruise control and lane keeping functions are introduced in detail. Experimental results acquired from high-fidelity 1/10-scale autonomous driving vehicles illustrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Traffic congestion wastes fuel and commuters’ time, and adds to CO2 emissions. Stop-and-go traffic instabilities can be suppresses using bilateral control—which differs from “car following” and adaptive cruise control in that, counter-intuitively, it uses information about the following vehicle (as well as about the leading vehicle). Stability can be proven mathematically, and can be demonstrated in simulation. A physical analog of a sequence of vehicles using bilateral control is a chain of masses connected by springs and dampers—a system which is inherently stable, since it lacks an external energy source. Here, in order to further understand bilateral control and its capacity to suppress instabilities, we move from a microscopic view (interaction of individual vehicles) to a macroscopic view (densities and flow rates). This leads us to the damped wave equation governing traffic under bilateral control. That equation allows us to determine the speed of propagation of disturbances, as well as their rate of decay. The equation is also useful in fine tuning parameters of bilateral control systems. [ABSTRACT FROM AUTHOR]

The objective of drift counteraction optimal control (DCOC) problem is to compute an optimal control law that maximizes the expected time of violating specified system constraints. In this paper, we reformulate the DCOC problem as a reinforcement learning (RL) one, removing the requirements of disturbance measurements and prior knowledge of the disturbance evolution. The optimal control policy for the DCOC is then trained with RL algorithms. As an example, we treat the problem of adaptive cruise control, where the objective is to maintain desired distance headway and time headway from the lead vehicle, while the acceleration and speed of the host vehicle are constrained based on safety, comfort, and fuel economy considerations. An informed approximate Q-learning algorithm is developed with efficient training, fast convergence, and good performance. The control performance is compared with a heuristic driver model in simulation and superior performance is demonstrated. [ABSTRACT FROM AUTHOR]

A vehicle traffic predictive cruise control (TPCC) system, responding to the change of downstream traffic situation, has been proposed to improve traffic operation and the fuel efficiency of vehicle based on the asymmetric traffic theory. The proposed predictive cruise control system consists of four parts: 1) deceleration-based safety surrogate measure (DSSM); 2) single-vehicle control algorithm; 3) multivehicle safety measurement (Co-DSSM); and 4) TPCC. The single-vehicle control algorithm basically decides the acceleration action based on estimated safety state between a subject vehicle and the immediate preceding vehicle, and the control strategy is determined by asymmetric driving behavior. Then, TPCC adjusts the amount of acceleration based on the Co-DSSM from multiple downstream vehicles, which contains the information on future traffic condition of the subject vehicle. A simulation using the real vehicle trajectories from the Next Generation Simulation (NGSIM) data validates the proposed TPCC system, and we compare the results with the real vehicles' car-following patterns. It is found that the proposed TPCC system can contribute to both the energy consumption and traffic flow operation by obtaining a higher level of traffic stability. Such results are due to the effects of suppressing the shockwave from downstream traffic and removing the unnecessary deceleration and acceleration actions. [ABSTRACT FROM PUBLISHER]