Adaptive Cruise Control System Research Articles

Mobility Digital Twin: Concept, Architecture, Case Study, and Future Challenges

A Digital Twin is a digital replica of a living or nonliving physical entity, and this emerging technology attracted extensive attention from different industries during the past decade. Although a few Digital Twin studies have been conducted in the transportation domain very recently, there is no systematic research with a holistic framework connecting various mobility entities together. In this study, a mobility digital twin (MDT) framework is developed, which is defined as an artificial intelligence (AI)-based data-driven cloud–edge–device framework for mobility services. This MDT consists of three building blocks in the physical space (namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Human</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vehicle</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Traffic</i> ), and their associated Digital Twins in the digital space. An example cloud–edge architecture is built with Amazon Web Services (AWS) to accommodate the proposed MDT framework and to fulfill its digital functionalities of storage, modeling, learning, simulation, and prediction. A case study of the personalized adaptive cruise control (P-ACC) system is conducted, which integrates the key microservices of all three digital building blocks of the MDT framework: 1) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Human Digital Twin</i> with user management and driver type classification; 2) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vehicle Digital Twin</i> with cloud-based advanced driver-assistance systems (ADAS); and 3) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Traffic Digital Twin</i> with traffic flow monitoring and variable speed limit. Future challenges of the proposed MDT framework are discussed toward the end of the article, including standardization, AI for computing, public or private cloud service, and network heterogeneity.

Simulation-Based Analysis of "What-If" Scenarios with Connected and Automated Vehicles Navigating Roundabouts.

Despite the potential of connected and automated vehicles (CAVs), there are still many open questions on how road capacity can be influenced and what methods can be used to assess its expected benefits in the progressive transition towards fully cooperative driving. This paper contributes to a better understanding of the benefits of CAV technologies by investigating mobility-related issues of automated vehicles operating with a cooperative adaptive cruise control system on roundabout efficiency using microscopic traffic simulation. The availability of the adjustment factors for CAVs provided by the 2022 Highway Capacity Manual allowed to adjust the entry capacity equations to reflect the presence of CAVs on roundabouts. Two mechanisms of entry maneuver based on the entry lane type were examined to compare the capacity target values with the simulated capacities. The microscopic traffic simulator Aimsun Next has been of great help in building the “what-if” traffic scenarios that we analysed to endorse hypothesis on the model parameters which affect the CAVs’ capabilities to increase roundabouts’ throughput. The results highlighted that the increasing penetration rates of CAVs have greater impacts on the operational performances of roundabouts, and provided a synthetic insight to assess the potential benefits of CAVs from an efficiency perspective.

Stability Analysis of Heterogeneous Traffic Flow with Connected and Automated Vehicles: Joint Consideration of Communication Failures and Driver Takeover

As a primary stage of connected and automated vehicles (CAVs), the cooperative adaptive cruise control (CACC) system takes some outstanding advantages such as accurate perception and timely reaction, which benefit from vehicular communication, over human-driven vehicles (HDVs). However, these advantages will turn into stability risks in case of communication failures caused by malicious attacks. Thus, this study aims at analyzing the stability of heterogeneous traffic flow with communication failures. We model two types of communication failures: bogus messages and transmission delay, and introduce drivers’ takeovers to react to communication failures. As a result, heterogeneous traffic flow consists of normal CACC vehicles, CACC vehicles with communication failures, and HDVs. Then, a series of numerical analyses, including startup and braking analysis, incidents analysis, and density wave, are proposed to verify the theoretical models and demonstrate their major properties. Besides, a discussion on traffic capacity is presented to analyze the overall impact of communication failures on traffic flow characteristics. The findings can help to investigate the stability evolution of heterogeneous traffic flow and determine the appropriate traffic flow configuration under communication failures.

Cut through traffic like a snake: cooperative adaptive cruise control with successive platoon lane-change capability

A cooperative adaptive cruise control (CACC) system may be impeded by slow-moving traffic in the application. To improve the mobility of CACC, this research proposes a CACC controller with successive platoon lane-change capability. The goal is to help a platoon cut through traffic successively like a snake via smaller windows. The proposed controller has the following features: i) with successive platoon lane-change capability; ii) with string stability and lateral stability; iii) with consideration of vehicle dynamics. The proposed controller is evaluated on a simulation platform with the context of traffic and a joint simulation platform consisting of PreScan and Matlab/Simulink. the Results demonstrate that compared to the conventional controller: i) platoon lane-change competence is enhanced by 71.36% on arterials and 120.49% on freeways; ii) platoon lane-change efficiency is enhanced by 25.05% on arterials and 41.36% on freeways; iii) the proposed controller is more robust against congestion. Moreover, the computation time of the proposed controller is approximately 15 milliseconds when running on a laptop equipped with an Intel i7-8750H CPU. This indicates that the proposed controller is ready for real-time implementation.

Car-Following Characteristics of Commercially Available Adaptive Cruise Control Systems and Comparison With Human Drivers

Adaptive cruise control (ACC) system, as one of the most fundamental modules of automated vehicles, is widely used in commercially available vehicles. It inevitably influences the traffic flow, from both the individual perspective, that is, its interaction with other traffic participants, and the traffic system perspective, that is, traffic string stability and road capacity. However, subject to limited data availability, no consistent conclusions on these impacts have been reached in the literature. Meanwhile, the similarities and differences between ACC vehicles and human-driven vehicles (HDV) have not been fully discussed and comparisons among different commercially available ACC systems remain to be untangled. Therefore, to fill this gap, this study investigates the car-following characteristics of various ACC systems and compares them with human drivers based on the open-access OpenACC database. We first identify the proper surrogate car-following model for denoting the driving behaviors of ACC vehicles and HDVs from five widely used car-following models, among which the best-fitted one is the intelligent driver model. Then, we implement the Gaussian mixture model and Jensen-Shannon divergence to describe the similarities between ACC systems and human drivers. Moreover, the string stability of different ACC platoons in various traffic conditions are investigated with a series of simulation experiments. Results show that all the ACC systems are string unstable, and more unstable than human drivers. The behavior of the Ford-ACC system is most similar to human drivers with a relative low instability, while the Peugeot-ACC system behaves most differently to human drivers, and aggressively with the highest instability.

Optimal Trajectory Planning of Connected and Automated Vehicles at On-Ramp Merging Area

Cooperative Adaptive Cruise Control (CACC) systems can significantly improve traffic safety and roadway capacity utilizing short following gaps of vehicles enabled by inter-vehicle communications. However, due to merging processes occurring at freeway merging areas, existing CACC operation approaches are generally not applicable and the operation will have to revert back to Adaptive Cruise Control (ACC) or human-driven mode, which in turn will result in a capacity drop. This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging. Firstly, a control framework of the CACC is adopted for a longitudinal control of CAVs, which helps individual CAVs to join platoons and to maintain platoon operations. Secondly, to ensure smooth lane-changing executions while achieving stable platoons, an optimal controller that considers lane-changing motivation of merging vehicles and impact of merging on platoons, is proposed. Third, a Legendre pseudo-spectral algorithm is applied to transform the controller into a simpler nonlinear programming problem and to efficiently solve it. Simulation assessments of the proposed method are conducted at both individual vehicle level and traffic-flow level. At the individual vehicle level, the proposed method has the potential to improve the traffic safety without compromising fuel consumption and emissions compared with unoptimized feasible schemes. At a traffic-flow level, an online evaluation platform is implemented, and a typical freeway on-ramp area is studied. The simulation results have demonstrated that the proposed controller provides significant improvements in terms of efficiencies in traffic operations.

Introducing the Effects of Road Geometry Into Microscopic Traffic Models for Automated Vehicles

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%).

Low Sidelobe Series-Fed Patch Planar Array with AMC Structure to Suppress Parasitic Radiation

For automobile radar systems, the antenna array requires a low sidelobe level (SLL) to reduce interference. A low-SLL and low-cost planar antenna array are proposed in this article for millimeter-wave automotive radar applications. The proposed array consists of six linear series-fed patch arrays, a series distribution network using a grounded co-planar waveguide (GCPW), and a bed of nails. First, a hybrid HFSS-MATLAB optimization platform is set up to easily obtain good impedance matching and low SLL of the linear series-fed patch array. Then, a six-way GCPW power divider is designed to combine the optimized linear sub-array to achieve a planar array. However, since CCPW is a semi-open structure, like a microstrip line, the parasitic radiation generated by the GCPW feeding network will lead to the deterioration of the SLL. To solve this problem, a bed of nails—as an artificial magnetic conductor (AMC)—is designed and placed above the feeding networking to create an electromagnetic stopband in the working band. Its working mechanism has been explained in detail. The feeding network cannot effectively radiate electromagnetic waves into free space. Thus, the parasitic radiation can be suppressed. A low-SLL planar array prototype working at 79 GHz is designed, manufactured, and measured. The measured results confirm that the proposed low-SLL planar array has a −10 dB impedance bandwidth of 3 GHz from 77 to 80 GHz and a maximum peak gain of 21 dBi. The measured SLL is −24 dB and −23 dB in the E-plane and H-plane at 79 GHz, respectively. The proposed low SLL array can be used for adaptive cruise control (ACC) system applications.

Congestion-mitigating MPC design for adaptive cruise control based on Newell’s car following model: History outperforms prediction

Currently, model predictive control (MPC) for adaptive cruise control (ACC) systems relies on the prediction of the leader’s motion to plan the follower’s trajectory. However, such predictions must be accurate to guarantee string stability, which represents an ongoing challenge for machine learning approaches. This issue can be circumvented by simply incorporating the leader’s history, which follows from Newell’s car-following (CF) model where a trajectory under congestion corresponds to a temporal–spatial shift of the leader’s past trajectories. By leveraging this insight, this paper develops a family of MPC models based on Newell’s CF model, labeled Newell MPCs, which are safe and can reduce traffic congestion.Specifically, We first present baseline Newell MPCs to replicate the original Newell’s CF model, including the Xbound-Model, which uses the shifted leader trajectory as an upper envelope; and the Xref-Model, which adopts the shifted leader trajectory as a reference to avoid the issue of infeasible solution triggered by hard constraints. To further improve the control performance, we propose the XV-Model which uses the leader speed history as an additional reference to enhance the model robustness and regulate speed over/under-shootings. In addition, we extend the single-leader Newell’s model through incorporating multiple leaders and propose the Xmul-Model, which can achieve driver anticipation, and correspondingly reduce reaction time and improve string stability. Finally, based on the XV-Model, we present two additional extensions: (i) the XVrelax-Model, which incorporates driver relaxation to achieve smooth response to merging traffic; and (ii) the XVss-Model, which achieves strict string stability to further dampen traffic oscillations. The proposed Newell MPCs are tested using both numerical simulations and field studies on a stock 2019 Honda Civic using Openpilot and Comma.ai; the source code is available at https://github.com/HaoZhouGT/openpilot.

Research on Lane Detection of Intelligent Vehicle Based on Single Camera

The detection of lane lines is the basis of lane departure warning system, adaptive cruise control and blind spot monitoring system, and its application research has attracted more and more attention from researchers. Based on computer vision, this paper designs a real-time lane detection and recognition algorithm. Firstly, the original image is pre-processed to remove the interference information and reduce the amount of information calculation. Then, according to the principle of edge detection technology and threshold segmentation technology, the possible lane line areas are segmented. Finally, based on the analysis of the lane structure and direction attributes, the lanes that match the attributes in the possible lane area are obtained. The experimental results show that the method has better accuracy, real-time and robustness than the traditional algorithm, and has certain reference significance for the development of auxiliary driving and unmanned driving.

Hardware Loop Simulation of Distributed Embedded Integrated Circuits

In order to improve the performance of the automotive active safety system, the author proposes a distributed embedded ACCS hardware loop simulation system. The automotive adaptive cruise control system (ACCS) is a completely distributed embedded control system. The author adopts the method of hardware loop simulation, an ARM-based automotive ACCS simulation platform is designed, and the hardware structure and software design of the platform are introduced. The simulation results show that the performance of single-node and double-node responses to step change is basically the same. There is a network in the two-node control loop, the carrying capacity and communication bandwidth of the network are limited, which will inevitably cause information collision and retransmission, which will inevitably cause a delay in the information transmission process, with a delay of about 4.5 seconds. The system opens up the design idea of ACCS and also lays a foundation for future research in related directions, which has a certain enlightenment effect.

Do adaptive cruise control and lane keeping systems make the longitudinal vehicle control safer? Insights into speeding and time gaps shorter than one second from a naturalistic driving study with SAE Level 2 automation

Advanced driver assistance systems such as adaptive cruise control (ACC) and lane keeping system (LKS) potentially contribute to reducing crash rates and traffic congestion. On-road studies based on early ACC systems operational at medium–high speeds only have shown that the system reduces the proportion of short time gaps when activated. Despite the effects on driver behaviour, most mathematical models assessing the impact of ACC and LKS systems on crash rates and traffic congestion are not based on empirical findings.This study examines the factors that influence changes in the longitudinal vehicle control when driving with ACC and LKS. The data were collected in a naturalistic driving experiment with full-range ACC and LKS and two different vehicle brands (BMW and Tesla) in the Netherlands. To capture changes that are relevant for traffic safety, speeding and a time gap shorter than one second were investigated. The factors influencing speeding and short time gaps were analysed using statistical tests and logistic regression models with random effects, that allow to control for the impact of different explanatory variables and correlations between repeated 10-s intervals over time.The findings revealed that, overall, drivers were less likely to speed and they were also less likely to have a time gap shorter than one second in the experimental condition with the ACC and the LKS than in the baseline condition in manual driving. Drivers were likely to speed in the following 10-s interval when the current speed was close to the speed limit, and/or when the next speed limit was lower than the current speed limit, and/or when the acceleration was high. Drivers were likely to have a short time gap in the following 10-s interval when approaching a slower leader, and/or when the current time gap was short and/or when the acceleration was high. Controlled for these main factors, drivers were less likely to speed and to have a short time gap when the ACC and the LKS were active. However, drivers were more likely to speed when overruling the ACC by pressing the gas pedal. When the systems were active, one vehicle brand showed a smaller probability of a short time gap than the other brand, suggesting differences in ACC system settings between brands. In addition, the speeding probability increased while the probability of a short time gap decreased over time during the trip after the activation of the systems. Although further studies including a larger sample of participants and a wider range of traffic situations are needed, the results are useful to the design of automated vehicles that prevent speeding and short time gaps, and to the implementation of traffic simulations that evaluate the impact of ACC and LKS on crash rates and traffic congestion according to realistic on-road data.

Significance of low-level control to string stability under adaptive cruise control: Algorithms, theory and experiments

Commercial adaptive cruise control (ACC) systems are bi-level: an upper-level planner decides the target trajectory and the low-level system executes it. Existing literature on ACCs mostly focus on the planner algorithms or the actuator delay, while the transition process between them, e.g. the low-level control design and its impact are often ignored. This paper tries to fill this gap by digging into the codebase of a recent open-source self-driving system, Openpilot (OP), Comma.ai, from which we extract and formulate the algorithms at both the upper and lower levels. For linear ACCs, the paper extends the transfer function analysis from planners only to full control loops and investigates the impact of slow/fast low-level control on the overall string stability (SS). For MPC ACCs, it studies their planning characteristics based its optimization objectives and approximates the low-level impact using an ODE approach.We find that low-level control has a significant impact on the overall SS of ACCs: (i) slow low-level control undermines SS under small frequencies and improves SS given large frequencies for linear systems, (ii) MPC features a varying gain throughout an oscillation, where the fast low-level control typically results in a ‘fast-slow’ changing process of the MPC gain, which benefits the SS, whereas the slow low-level control leads to a ‘slow-fast’ varying gain which undermines the SS, (iii) slow low-level control are common as they arise from comfort-oriented control gains, from a ”weak” actuator performance or both, and (iv) the SS is very sensitive to the integral gain under slow low-level control for both PI and PIF controllers. Overall, the study recommends fast low-level control for ensuring vehicular SS to reduce traffic congestion, considering that large congestion waves usually feature both small frequencies and large amplitudes, although slow controllers could perform even better provided a short and small leader perturbation. The findings of this paper are verified both numerically and experimentally. For the first time in the literature we implement custom ACC algorithms on market cars, and achieve SS on open roads with a random leader by only tuning the low-level controllers. The source code is shared at https://github.com/HaoZhouGT/openpilot to support on-road experiments of arbitrary car-following models, which may be of interest to other studies.

Assessment of Dangerous Traffic Situations for Autonomous Vehicles

This article will show some dangerous situations with autonomous vehicles. The main question in this article is how does the ACC (Adaptive Cruise Control) as an autonomous vehicle function work? In which cases must the driver act and how. The research showed some real life examples based on a Euro NCAP test. The car manufacturers say that the modern ACC systems can be well used in urban traffic, especially in traffic jam, but there can be some critical situations which can cause an accident if the driver does not act. These situations are collected in this article. The main aim of the article is to identify a methodological framework for estimating the proper operation conditions related to the analyzed automated driving assistant systems.

A new speed planning method based on predictive curvature calculation for autonomous driving

As the number of vehicles in traffic is increasing day by day, the accident rates and driving effort are significantly raising. For this reason, ensuring safety and driving comfort is becoming more and more important. Driver assistance systems are the most common systems that are adopted for this purpose. With the development of technology, lane tracking support and adaptive cruise control systems are now being sold as standard equipment. More advanced research is being done for fully autonomous driving. One of the most critical parts of autonomous driving is speed profile planning. In this paper, a curvature-based predictive speed planner is designed using a fuzzy logic strategy. In addition to the predictive curvature value, lateral error to the planned path is considered for the final decision. With the help of the proposed predictive nature, which is not included in classical curvature-based planners, the vehicle is able to reduce the speed before cornering. Similarly, it starts increasing the speed when it is still in a curve but very near to the straight part of the road. In order to illustrate the efficiency of the proposed method and compare it with other approaches, the simulations are performed using realistic vehicle dynamics models of CarMaker software. After the comparative analysis, the designed planner is utilized on a real 1/10 scaled autonomous vehicle platform to show its real-world performance. The results show that the proposed speed planner is an effective and promising algorithm for both the comfort and path tracking performance.

Multianticipation for string stable Adaptive Cruise Control and increased motorway capacity without vehicle-to-vehicle communication

Adaptive Cruise Control (ACC) systems have been expected to solve many problems of motorway traffic. Now that they are widespread, it is observed that the majority of existing systems are string unstable. Therefore, small perturbations in the speed profile of a vehicle are amplified for the vehicles following upstream, with negative impacts on traffic flow, fuel consumption, and safety. Increased headway settings provide more stable flow but at the same time it deteriorates the capacity. Substantial research has been carried out in the past decade on utilizing connectivity to overcome this trade-off. However, such connectivity solutions have to overcome several obstacles before deployment and there is the concrete risk that motorway traffic flow will considerably deteriorate in the meanwhile. As an alternative solution, the paper explores multianticipation without inter-vehicle communication, taking advantage of the recent advancements in the field of RADAR sensing. An analytical study is carried out, based on the most widely used model and parameter settings used to simulate currently available commercial ACC systems, comparing the transfer functions and step responses for the nominal and the multianticipative formulations. Then, a microsimulation framework is employed to validate our claim on different speed profiles. Analytical results demonstrate that multianticipation enhances stability without impacting traffic flow. On the contrary, the simulation study indicates that the multianticipative-ACC can produce higher road capacity even in the presence of external disturbances and for a wide range of calibrated parameters. Finally, optimality conditions for the tuning of the headway policy are derived from a Pareto optimization.

An improved sliding mode control (SMC) approach for enhancement of communication delay in vehicle platoon system

Vehicle platoon systems are widely recognized as key enablers to address mass-transport. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) are two technologies that drive platooning. The inter-vehicle spacing and collaboration velocity in the platoon are important parameters that must be controlled. A new mass-transport system called the Tracked Electric Vehicles (TEV) has been proposed which has reduced the inter-vehicular spacing to only a quarter of the regular car length. This enables mass transport at uniform speed for cars with speed of 200km/h. However, conventional radar based adaptive cruise control (ACC) system fail to control each vehicle in these scenarios. Lately, sliding mode control (SMC) has been applied to control platoons with communication technology but with low speed and without delay. This paper proposes a novel SMC design for TEV using global dynamic information with the communication delay. Also, graph theory has been employed to investigate different V2V communication topology structures. To address issues of node vehicle stability and string stability, Lyapunov candidate function is chosen and developed. Additionally, this paper uses first-order vehicle models with different acceleration/deceleration parameters for simulation validations under communication delay. The results show that this SMC has a significant tolerance ability and meets the design requirements of TEV.

Physics-augmented models to simulate commercial adaptive cruise control (ACC) systems

This paper investigates the accuracy and robustness of car-following (CF) and adaptive cruise control (ACC) models in reproducing measured trajectories of commercial ACCs. To this aim, a general modelling framework is proposed, in which ACC and CF models have been incrementally augmented with physics-based extensions: namely, perception delay, linear or nonlinear vehicle dynamics, and acceleration constraints. This framework has been applied to the Intelligent Driver Model (IDM), Gipps’ model, and to three basic ACC algorithms. These are linear controllers which are coupled with a constant time-headway spacing policy, and with two other policies derived from the traffic flow theory: the IDM desired distance function, and Gipps’ equilibrium distance-speed function. The ninety models resulting from the combination of the five base models with the aforementioned extensions, have been assessed and compared through a vast calibration and validation experiment against measured trajectory data of vehicles driven by ACC systems. Overall, the study has shown that physics-based extensions provide limited improvements to the accuracy of existing models. In addition, if an investigation against measured data is not carried out, it is not possible to argue which extension is the most suited for a specific model. The linear controller with Gipps’ spacing policy has resulted the most accurate model, while the IDM the most robust to different input trajectories. Eventually, all models have failed to capture the behaviour of some car brands – just as models fail with some human drivers. Therefore, the choice of the “best” model is independent of the car brand to simulate.

Providing robust-adaptive fractional-order sliding mode control in hybrid adaptive cruise control systems in the presence of model uncertainties and external disturbances

Providing robust-adaptive fractional-order sliding mode control in hybrid adaptive cruise control systems in the presence of model uncertainties and external disturbances

A framework of safety analysis with temporal feature based on MBSA and case study for ACC system

The safety of automotive Adaptive Cruise Control (ACC) system is of great significance to prevent fatigue driving, improve driving comfort, reduce accident rate and promote the development of intelligent transportation and autonomous driving technology. However, the current safety analysis of ACC lacks consideration of the temporal dynamic property, so it is necessary to establish a set of safety analysis methods to consider the temporal characteristics. This paper proposes a new safety analysis method based on MBSA framework and introduces temporal features. Altarica3.0 is a high-level modeling language for safety analysis, and its basic mathematical form is Guardian Transformation System (GTS). In this paper, we outline an analysis approach that converts failure behavioral models (GTS) to temporal fault trees (TFTs), which can be analyzed using Pandora a recent technique for introducing temporal logic to fault trees. However, like classical fault tree analysis, TFT analysis requires a lot of manual effort, which makes it time consuming and expensive. In order to improve the safety of the system, the proposal extends Bayesian Networks with Pandora and results to dependability analysis with temporal relationships to provide more reliable basis for safety design. As a typical case study, the safety analysis method proposed in this paper is applied to the safety analysis of adaptive cruise system, and the results show the effectiveness of the proposed method. Furthermore, it also provides new technologies for the automation and intelligence of safety analysis for smart internet of vehicle.