Automated Vehicle Applications Research Articles

Development, Modeling and Assessment of Connected Automated Vehicle Applications

Development, Modeling and Assessment of Connected Automated Vehicle Applications

Enabling Analysis, Modeling, and Simulation for Cooperative Automated Vehicle Applications in Transportation Systems: The Pillar Diagram

Enabling Analysis, Modeling, and Simulation for Cooperative Automated Vehicle Applications in Transportation Systems: The Pillar Diagram

Roadside LiDAR Sensor Configuration Assessment and Optimization Methods for Vehicle Detection and Tracking in Connected and Automated Vehicle Applications

This paper develops an assessment and optimization model for configuring roadside LiDAR (Line Detection and Ranging) installation. More specifically, an analytic and a simulation model have been developed to analyze the detection blind zones and their impact on vehicle detection and tracking capabilities in Connected and Automated Vehicle (CAV) applications. The proposed model can derive the area and height of the detection blind zones from a given roadside LiDAR location and road geometry. Evaluation metrics are also proposed to assess the severity of the blind zone including laser beam density, blind zone height and duration, vehicle trajectory missing rate and duration. The simulation model can be used to evaluate and identify optimal configurations for different installation scenarios. To validate the proposed model, the 15-min US101 NGSIM (Next Generation SIMulation) dataset was used for validating the proposed model. Different configuration settings were simulated and compared. The evaluation results demonstrate the capabilities of the proposed models in planning for optimal roadside LiDAR sensor installation for vehicle detection and tracking.

Online inference of lane changing events for connected and automated vehicle applications with analytical logistic diffusion stochastic differential equation

Inferences of the lane changing behaviors (including lane changing initiation moment LCIM, cross-lane-mark moment CLMM, and lane changing duration LCD) of the surrounding vehicles and taking actions to avoid collision are essential for the safe operation of connected and autonomous vehicles (CAVs). The majority of current models rely on data-driven methods that need to be trained before deployment. Besides, an analytical and stochastic formulation of the lateral model which can generate CLMM and LCD distribution and adjust the parameters online is still lacking. To the best of our knowledge, currently, no method can provide an analytically stochastic lateral movement that explicitly considers the system noise and simultaneously outputs the information of lane changing initiation moment, cross-lane-mark moment, and lane changing duration in a real-time manner. To fill such a gap, a stochastic lateral trajectory framework with parsimonious parameters is established and an online simultaneous inference of LCIM, CLMM, and LCD is developed. The proposed method is tested with the highD and NGSIM datasets. The results show that 1) computational efficiency-wise, the algorithm takes milliseconds to run, which suggests promising prospects for field deployment; 2) the false negative and false positive errors of the LCIM inference are as low as 1.5%, indicating that the method is robust to the stochastic noise; 3) LCIM inference is insensitive to the lane width, which means the model is error tolerant when such information is lacking; and 4) the error of CLMM inference is within 2 sec, while most of the error of LCD locates within 4 sec, suggesting satisfactory performance.

Stop Sign Gap Assist Application in a Connected Vehicle Simulation Environment

Assessment of the safety and mobility impacts of connected vehicles (CVs) and cooperative automated vehicle applications is critical to the success of these applications. In many cases, there may be trade-offs in the mobility and safety impacts depending on the setting of the parameters of the applications. This study developed a method to evaluate the safety and mobility benefits of the Stop Sign Gap Assist (SSGA) system, a CV-based application at unsignalized intersections, which utilizes a calibrated microscopic simulation tool. The study results confirmed that it was critical to calibrate the drivers’ gap acceptance probability distributions in the utilized simulation model to reflect real-world driver behaviors when assessing SSGA impacts. The simulation models with the calibrated gap parameters were then used to assess the impacts of the SSGA. The results showed that SSGA can potentially improve overall minor approach capacity at unsignalized intersections by approximately 35.5% when SSGA utilization reaches 100%. However, this increase in capacity depended on the setting of the minimum gap time in the SSGA and there was a clear trade-off between capacity and safety. The analysis indicated that as the minimum gap time used in the SSGA increased, the safety of the intersection increased, showing for example that with the utilization of an 8-s gap at a 750 vph main street flow rate, the number of conflicts could decrease by 30% as the SSGA utilization rate increased from 0% to 100%.

State Estimation for Cooperative Lateral Vehicle Following Using Vehicle-to-Vehicle Communication

A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to ensure accurate and stable vehicle following behavior. Control schemes for the cooperative control of longitudinal and lateral vehicle dynamics generally require vehicle state information about the lead vehicle, which in some cases cannot be accurately measured. Including vehicle-to-vehicle communication in the state estimation process can provide the required input signals for the practical implementation of cooperative control schemes. This study is focused on demonstrating the benefits of using vehicle-to-vehicle communication in the state estimation of a lead and following vehicle via simulations. The state estimator, which uses a cascaded Kalman filtering process, takes the operating frequencies of different sensors into account in the estimation process. Simulation results of three different driving scenarios demonstrate the benefits of using vehicle-to-vehicle communication as well as the attenuation of measurement noise. Furthermore, in contrast to relying on low frequency measurement data for the input signals of cooperative control schemes, the state estimator provides a state estimate at every sample.

Fog-Based Distributed Networked Control for Connected Autonomous Vehicles

With the rapid developments of wireless communication and increasing number of connected vehicles, Vehicular Ad Hoc Networks (VANETs) enable cyberinteractions in the physical transportation system. Future networks require real-time control capability to support delay-sensitive application such as connected autonomous vehicles. In recent years, fog computing becomes an emerging technology to deal with the insufficiency in traditional cloud computing. In this paper, a fog-based distributed network control design is proposed toward connected and automated vehicle application. The proposed architecture combines VANETs with the new fog paradigm to enhance the connectivity and collaboration among distributed vehicles. A case study of connected cruise control (CCC) is introduced to demonstrate the efficiency of the proposed architecture and control design. Finally, we discuss some future research directions and open issues to be addressed.

Evaluating the Effectiveness of Integrated Connected Automated Vehicle Applications Applied to Freeway Managed Lanes

The purpose of this study is to define an operational concept involving connected automated vehicle (CAV) operation on freeway managed lanes. Despite the low projected market penetration of CAVs during the next decade, the use of managed lane facilities has the potential to support the realization of increased mobility benefits by their very nature. The proposed CAV operation involves platoons of equipped vehicles governed by integrated CAV applications, including cooperative adaptive cruise control (CACC), cooperative merge, and speed harmonization. This study proposes an algorithm for integrating CAV applications. Through microscopic simulation, the study particularly examines the effectiveness of CACC, CACC plus cooperative merge, and the addition of speed harmonization under different penetration rates. Simulation results show the effectiveness of the bundled application to enhance system throughput and reduce delay, even with low CAV penetration rates. The speed harmonization shows the greatest effects on delay reduction at medium-to-high penetration rates and some benefits even at low penetration rates. The conclusions provide operational insights and guidance for traffic management centers to implement CAV-based traffic control in the future.

Leveraging existing high-occupancy vehicle lanes for mixed-autonomy traffic management with emerging connected automated vehicle applications

Transportation agencies have started including phased deployment of connected and automated vehicle (CAV) technologies in their transportation plans and programs. While theoretical analyses have indicated significant benefits of CAVs for improving system performance, deploying these technologies at existing or adapted highway facilities concerns more than technological issues. This study introduces the concepts and operational strategies of using managed lanes, High-Occupancy Vehicle (HOV) lanes in particular, for mixed-autonomy traffic management. The study enhances existing and develops new algorithms for three selected freeway CAV applications as a CAV technology integration, including speed harmonization, cooperative adaptive cruise control (CACC), and cooperative merge. Instead of evaluation on a synthetic segment, this study reports a large-scale simulation-based real-world case study to investigate CAV early deployment opportunities on Interstate 66 outside the Beltway. The simulation results show that, for all scenarios, individual and bundled CAV applications can significantly improve traffic performance in terms of delay and throughput. CACC platooning is the most effective individual strategy to improve traffic performance. The bundled CAV applications can benefit the system, even with low CAV market penetration, and fully handle more than 130 percent of the existing demand with high CAV market penetration rates. Additionally, left-side dedicated ramps and shared managed lane operational strategies are also beneficial, even during early deployment stages.

A Dynamic Adaptive Algorithm for Merging Into Platoons in Connected Automated Environments

The automated merging control is one of the connected and automated vehicle applications that is expected to improve the operation and safety of freeway merging areas. This study introduces a dynamic adaptive algorithm for automated merging control based on a cost function of travel time for both on-ramp and mainline vehicles. The developed algorithm is designed to guide on-ramp vehicles to merge efficiently, without frequent slowdown or wait for merging gaps at the end of the ramp and with minimal disruption to the mainline traffic. The algorithm was evaluated under different mainline traffic demands, ranging from light to heavy conditions, using a simulation model for a one-mile freeway segment with two lanes and a single-lane on-ramp. Platooning is implemented in the simulation based on Cooperative Adaptive Cruise Control. The performance of the algorithm is compared to a base case where mainline vehicles cooperatively decelerate to help on-ramp vehicles merge. The results show that the proposed algorithm reduced ramp delays in the range of 38% to 91% for different traffic conditions, without disrupting mainline operation. Additionally, travel time reliability index improved in the range of 18% to 48% under different traffic conditions. According to the simulation results, the proposed automated merging algorithm is henceforth considered reliable, with its promising performance.

Hardware-in-the-Loop Testing of Connected and Automated Vehicle Applications: A Use Case for Queue-Aware Signalized Intersection Approach and Departure

Most existing studies on connected and automated vehicle (CAV) applications apply simulation to evaluate system effectiveness. Model accuracy, limited data for calibration, and simulation assumptions limit the validity of evaluation results. One alternative approach is to use emerging hardware-in-the-loop (HIL) testing methods. HIL test environments enable physical test vehicles to interact with virtual vehicles from traffic simulation models, providing an evaluation environment that can replicate deployment conditions at early stages of CAV technology implementation without incurring excessive costs related to large field tests. In this study, a HIL testing system for vehicle-to-infrastructure (V2I) CAV applications is developed. The involved software and hardware includes a physical CAV controlled in real time, a traffic signal controller, communication devices, and a traffic simulator (VISSIM). Such HIL systems increase validity by considering the physical vehicle’s trajectories—which are constrained by real-world factors such as GPS accuracy, communication delay, and vehicle dynamics—in a simulated traffic environment. The developed HIL system is applied to test a representative early deployment CAV application: queue-aware signalized intersection approach and departure (Q-SIAD). The Q-SIAD algorithm generates recommended speed profiles based on the vehicle’s status, signal phase and timing (SPaT), downstream queue length, and system constraints and parameters (e.g., maximum acceleration and deceleration). The algorithm also considers the status of other vehicles in designing the speed profiles. The experiment successfully demonstrated this functionality with one test CAV driving through one intersection controlled by a fixed-timing traffic signal under various simulated traffic conditions.

Utilizing Model-Based Communication and Control for Cooperative Automated Vehicle Applications

Connected vehicle applications rely on wireless communication for achieving real-time situational awareness and enabling automated actions or driver warnings. Given the constraints of the communication systems, it is critical to employ communication strategies and approaches that allow robust situational awareness. In this work, we utilize the recently introduced concept of model-based communication and design a new strategy based on small- and large-scale modeling of vehicle movement dynamics. Our approach is to describe the small-scale structure of the remote vehicle movement (e.g., braking, accelerating) by a set of dynamic models (ARX models) and represent the large-scale structure (e.g., free following, turning) by coupling these ARX models together into a Markov chain. The effect of this design is investigated for the case of cooperative adaptive cruise control application. Assuming model-based communication approach is used with the coupled model, a novel stochastic model predictive method is proposed to achieve cruise control goals. We use actual highway driving maneuvers to compare the proposed methodology with the conventional design of cooperative adaptive cruise control.

Interlinking Vissim and ns-3 for Connected Vehicle Simulation: Case Study of Intelligent Dilemma Zone Avoidance

The effects of realistic wireless communication are important for the modeling and evaluation of emerging connected and automated vehicle applications. This paper discusses the development of a novel closed-loop connected vehicle analysis system (CONVAS) interlinking Vissim microscopic traffic simulation and ns-3 wireless communication simulation. In CONVAS, ns-3 uses Vissim’s vehicle positions for wireless modeling and in turn Vissim uses the packet reception from ns-3 to modify vehicle behaviors. The authors propose an application for intelligent avoidance of the dilemma zone to demonstrate the use of the platform for modeling connected and automated vehicles and to illustrate the effects of communications on the application performance. A simulation experiment was conducted with a test bed for an isolated high-speed, fixed-time signalized intersection with different communication model configurations. The evaluation results showed that the effectiveness of the application as measured by successful clearing of vehicles from the dilemma zone depended greatly on performance of wireless communication.

Multi‐sensor tracking and lane estimation in highly automated vehicles

Highly automated driving brings the next generation of driver assistance systems for increased safety and comfort. Automated vehicles execute part of the driving tasks whereas the driver is still involved in controlling the vehicle. Higher degrees of automation pose more strict requirements for perception systems in terms of performance and robustness. The HAVEit EU project investigates the application and validation of highly automated vehicle systems, technologies that are going to have a great impact on transport of the future. The purpose of this study is to examine in detail the problem of multi-sensor fusion for target tracking and road environment perception in an automated vehicle application. A series of algorithms are described for solving the data association and track estimation problems, both at sensor and central levels. The techniques that are used for multi-sensor lane estimation are also presented. Finally, results from simulated and real-time tests are given to demonstrate the performance of the algorithm.

Fault tolerant nonlinear control with applications to an automated transit bus

This paper presents an integrated structure for a passive and active fault tolerant control (FTC) design approach in the framework of a robust nonlinear control technique called Dynamic Surface Control (DSC). As motivated by the automated vehicle application, we consider two categories of possible faults: pre-specified (a priori) and non-specified faults. It is first shown that DSC can be considered as a passive FTC approach in the sense that it gives simultaneous robust stability to a set of nonlinear systems even in the presence of model uncertainties and the pre-specified faults. Then, the non-specified fault is classified depending on the fault’s impact on the closed-loop system and isolatability from a fault detection and diagnosis (FDD) system. If a fault is both intolerable and isolatable, an active FTC approach is taken which includes FDD and controller reconfiguration. More specifically, trajectory reconfiguration is considered to accommodate the actuator fault, i.e., to compensate for the performance loss due to the fault within the framework of a switched hierarchical structure. Finally, the integrated structure for the longitudinal control of an automated transit bus is designed through the proposed method. Simulation results of the fault tolerant controller are shown for both single and multiple multiplicative faults. This controller was implemented on the California PATH transit buses in a demonstration of automated public transportation technology in San Diego, California in August of 2003.