Road Curvature Research Articles

Co-optimization strategies for connected and automated fuel cell hybrid vehicles in dynamic curving scenarios

The performance of speed planning and energy management for connected and automated fuel cell hybrid vehicles (CAFCHVs) in the curve directly affects the curve passage, operating safety and energy economy. However, the uncertainty of complex traffic conditions (such as the dynamic state of the preceding and ego vehicle, road adhesion coefficient, and curve radius) and the lateral stability of CAFCHV lead to the difficulty of online speed planning and energy management. To address this problem, a co-optimization strategy is implemented in this study. First, according to the stability condition of CAFCHV in the curve, the critical safe speed is obtained by phase plane analysis. In addition, combing the timeliness, future information of driving conditions, and the current state of preceding and ego-vehicles, the gradient-based model prediction control (GRAMPC) leveraging the fast projection gradient method is adopted to calculate the safe and optimal speed sequence. Meanwhile, the energy management strategy (EMS) based on the power ratio adaptive equivalent consumption minimization strategy (PR-AECMS) is utilized for energy distribution. A multi-objective performance function is introduced to evaluate the total cost of hydrogen consumption and battery life extension. The simulation results reveal that the proposed strategy can obtain a safe and optimal speed sequence when CAFCHV operates on the curve road. And compared with the mode of tracking the speed of the preceding vehicle, the hydrogen consumption, SOC, battery degradation, and total cost are reduced by 1.4%, 1.9%, 9.9%, and 1.8% regulated by the planning mode, respectively.

Evaluation of Automatic Prediction of Small Horizontal Curve Attributes of Mountain Roads in GIS Environments

Road curve attributes can be determined by using Geographic Information System (GIS) to be used in road vehicle traffic safety and planning studies. This study involves analyzing the GIS-based estimation accuracy in the length, radius and the number of small horizontal road curves on a two-lane rural road and a forest road. The prediction success of horizontal curve attributes was investigated using digitized raw and generalized/simplified road segments. Two different roads were examined, involving 20 test groups and two control groups, using 22 datasets obtained from digitized and surveyed roads based on satellite imagery, GIS estimates, and field measurements. Confusion matrix tables were also used to evaluate the prediction accuracy of horizontal curve geometry. F-score, Mathews Correlation Coefficient, Bookmaker Informedness and Balanced Accuracy were used to investigate the performance of test groups. The Kruskal–Wallis test was used to analyze the statistical relationships between the data. Compared to the Bezier generalization algorithm, the Douglas–Peucker algorithm showed the most accurate horizontal curve predictions at generalization tolerances of 0.8 m and 1 m. The results show that the generalization tolerance level contributes to the prediction accuracy of the number, curve radius, and length of the horizontal curves, which vary with the tolerance value. Thus, this study underlined the importance of calculating generalizations and tolerances following a manual road digitization.

Driving around bends with or without shoulders: The influence of bend direction

Paved shoulders have long been used to create “forgiving” roads where drivers can maintain control of their vehicles even when as they drift out of the lane. While the safety benefits of shoulders have been well documented, their effects on driver behavior around curves have scarcely been examined. The purpose of this paper is to fill this gap by assessing whether the addition of shoulders affects driver behavior differently as a function of bend direction. Driver behavior in a driving simulator was analyzed on left and right curves of two-lane rural roads in the presence and absence of 0.75-m and 1.25-m shoulders. The results demonstrated significant changes in drivers’ lateral control when shoulders were provided. In the absence of oncoming traffic, the shoulders caused participants to deviate more toward the inner lane edge at curve entry, at the apex and at the innermost position on right bends but not left ones. In the presence of oncoming traffic, this also occurred at the apex and the innermost position, leading participants to spend more time off the lane on right curves. Participants did not slow down in either traffic condition to compensate for steering farther inside, thereby increasing the risk of lane departure on right curves equipped with shoulders. These findings highlight the direction-specific influence of shoulders on a driver’s steering control when driving around bends. They provide arguments supporting the idea that drivers view paved shoulders as a new field of safe travel on right curves. Recommendations are made to encourage drivers to keep their vehicle within the lane on right bends and to prevent potential interference with cyclists when a shoulder is present.

Experimental methods to investigate the performance of the braking system using GPS and IMU equipment

Establishing procedures and equipment for more accurate evaluation of the brake system performances is a major goal for automakers, but also for researchers, test equipment manufacturers, racing drivers and their staff, or for experts and forensics in their activity of accidents reconstruction. Increasing the performance and complexity of new vehicle stability control systems as well as driver assistance systems require finding new solutions for measuring and determining braking and stopping time and space, as well as a lot of new parameters such as braking system response, driver’s reaction times, actual value of the traction grip, corrections due to the slope of the road, or to the differences between the actual speed and the one displayed on board. Changes in adhesion and normal load on each wheel contact patch that occur under certain driving conditions develop lateral forces and yaw that disrupt longitudinal dynamics and must be determined and considered. The lack of braking marks in the case of partially braked or unlocked wheel by the ABS system is also a major impediment to the correct and accurate reconstitution of the braking and stopping space or speed. This paper describes a series of experimental research carried out by the authors using a professional GPS device, IMU, infrared thermal camera and a series of sensors that allow to determine with high precision the trajectory, radius of road curvature and tilt, the vehicle real speed, longitudinal and lateral accelerations, yaw speed or drift angle. Based on these determinations, several braking and acceleration parameters were estimated in alignment, in curves or in ramp and slope, respectively in coast down mode.

Road-Curvature-Range-Dependent Path Following Controller Design for Autonomous Ground Vehicles Subject to Stochastic Delays

In this paper, we investigate the PID controller design problem of path following for an autonomous ground vehicle (AGV). Firstly, a bicycle model is adopted and a vehicle offset model from the target path is integrated to the bicycle model. The PID controller considers the vehicle longitudinal speed variation for adaption to the road curvature and the stochastic delay induced by the network communication. This is realized by transforming the tuning problem of proportional-integral-derivative (PID) gains for path following into a design problem of a static-output-feedback (SOF) controller for a time-delayed linear parameter varying (LPV) model form. A sufficient condition is adopted to guarantee the stability of the closed-loop system. In order to achieve better tracking performance, we propose a strategy in which the PID gains are piecewise constant and are dependent on the road-curvature ranges. The stability of the switched system is guaranteed via the common Lyapunov function method. Grey wolf optimizer (GWO) is employed to solve the optimization problem with maximum absolute tracking error as the optimization objective and stability condition and actuator dynamics as constraints. Both simulation results based on the CarSim-Simulink joint platform and hardware-in-loop experiment results are used to verify the effectiveness of the proposed control strategy.

Vehicle Detection Approach Adjusting Road Curves to Estimate Local Traffic Density under Real Driving Conditions

This paper has measured local traffic density under real driving conditions. The methodology employed is based on the traditional process of image recording. However, the conventional method has suffered from three typical limitations: long processing times, low vehicle detection performance, and inaccurate road length estimates. This study proposed two sequential approaches to resolve the three drawbacks. First, the you-only-look-once (YOLO) algorithm was used to shorten the computation time and enhance vehicle detection accuracy. Second, the vanishing point was identified during digital image processing to adjust the vertical and horizontal curves of roads, thereby calculating accurate road lengths. The proposed framework was applied to an arterial road in Seoul, South Korea. This approach was fast enough to measure local traffic density in real time. The performance of vehicle detection evaluated using a weighted mean average precision (mAP) was high at 88.73%. The accuracy of estimated road lengths evaluated using the Pearson correlation coefficient was also high at 0.97. Some co-benefits expected from the proposed method are also discussed.

Vehicle trajectory data extraction from the horizontal curves of mountainous roads

ABSTRACT Trajectory data is essential for understanding the driver-vehicle-road interaction which is crucial for safety and operational assessment. The video image-processing technique is useful to collect such data on the entire traffic stream. Existing techniques assume that the road is planar which limits the application of the video-based traffic data collection to the plain terrains. With the objective to collect trajectory data from mountainous terrain, the present study proposes an approach to convert the pixel coordinates into real-world coordinates. The hypothesis was that a non-planar scene could be converted into a sequence of piece-wise planar scenes with a separate projection matrix for each planar region. The analysis shows that the proposed method could effectively transform the pixel coordinates into the real-world coordinates. Comparison of the estimated and observed speed/path profiles indicate the adequacy of the proposed method in collecting video-based trajectory data from mountainous terrain.

Predictive Shift Strategy of Dual-Clutch Transmission for Driving Safety on the Curve Road Combined with an Electronic Map

Predictive Shift Strategy of Dual-Clutch Transmission for Driving Safety on the Curve Road Combined with an Electronic Map

A Novel Combined Decision and Control Scheme for Autonomous Vehicle in Structured Road Based on Adaptive Model Predictive Control

In the research of autonomous vehicles, most existing studies treat the decision/planning and control as two separate problems. This idea originates from robotics. But since there are essential differences between robot and autonomous vehicle, the structure in Robotics may not be suitable for autonomous vehicles. Considering decision/planning and control separately may affect the performance of autonomous vehicle under complex driving conditions. To fill in the research gap, this paper proposes a novel scheme which considers the local motion planning and control in a combined manner. Firstly, the local motion planning is transformed into the longitudinal control problem based on the proposed scenario adaptive MPC, by which the motion behavior (driving along the global path, car-following, lane-change) can be automatically decided. Then, the lateral MPC controller is designed to track the global path and conduct the local motion commands. To ensure the performance of the path tracking control and a smooth lane-change process simultaneously, an adaptive weight mechanism is introduced in the lateral controller. Comprehensive case studies including both straight and curve road are conducted based on Carsim-Simulink co-simulation platform. The results show that the proposed algorithm can not only ensure the vehicle safety in complex driving conditions, but also ensure that the vehicle can drive at its desired velocity as much as possible by intelligently judging the most proper motion behaviors.

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%).

Designing a Model of the Early Warning System on the Road Curvature to Prevent the Traffic Accidents

Road traffic safety in developing countries is a complex problem involving many factors such as humans, vehicles, structures, and the roads' environments. This study focuses on reducing traffic accidents for passenger busses, aiming to identify the causes and create a simple computer model to warn in areas like EWS using geometric road data. The survey was conducted at accident-prone locations and simulated the travel speed, road geometric, and traffic composition data. Results can be concluded that the rate is largely influenced by the radius of curvature, visibility, road gradient, and weather conditions. The concept of the EWS computer model was developed using web-based technology that can use to record the data instantly. The model had tested in another location to validate the field parameter and geometric road improvement.

Active Lane-Changing Control of Intelligent Vehicle on Curved Section of Expressway

In order to improve the intelligent vehicle lane-changing performance, an active lane-changing control algorithm is proposed considering the changes of road curvature and vehicle speed. Firstly, the vehicle dynamics model considering vehicle speed variation and lane-changing safety distance is established, and the expected lane-changing trajectory model under the curved road is designed simultaneously. Then, taking the yaw rate and longitudinal speed as the control objectives of lateral and longitudinal motions, respectively, the sliding-mode variable structure control method based on Lyapunov stability condition is adopted, and the trajectory tracking controller is designed by combining the inverted method to track the desired lane-changing trajectory. Finally, the lane-changing trajectory model and trajectory tracking controller are verified in simulation platform of CarSim/Simulink and hardware-in-the-loop (HIL) test bench. The results show that the proposed trajectory tracking control method can perform the lane-changing behavior well under different road curvatures and vehicle speeds while maintaining high trajectory tracking control accuracy.

Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance

This paper presents a constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads with infrastructure assistance. Specifically, this paper systematically formulates trajectory optimization problems in a spatial domain and a curvilinear coordinate. As an alternative of temporal domain and Cartesian coordinate formulation, our formulation provides the constrained trajectory optimization flexibility to describe complex road geometries, traffic regulations, and road obstacles, which are usually spatially varying rather than temporal varying, with assistances vehicle to infrastructure (V2I) communication. Based on the formulation, we first conducted a mathematical proof on the controllability of our system, to show that our system can be controlled in the spatial domain and curvilinear coordinate. Further, a multiobjective model predictive control (MPC) approach is designed to optimize the trajectories in a rolling horizon fashion and satisfy the collision avoidances, traffic regulations, and vehicle kinematics constraints simultaneously. To verify the control efficiency of our method, multiscenario numerical simulations are conducted. Suggested by the results, our proposed method can provide smooth vehicular trajectories, avoid road obstacles, and simultaneously follow traffic regulations in different scenarios. Moreover, our method is robust to the spatial change of road geometries and other potential disturbances by the road curvature, work zone, and speed limit change.

Road geometry perception without accurate positioning and lane information

Road geometry perception indicates road direction and drivable area for Lane Keeping System and Lane Departure Warning System in Advanced Driver Assistance Systems or routing and decision-making in Autonomous Driving. High-definition map with accurate positioning or lane information is generally required. However, these two types of information are not always available because of positioning failure or lane mark degradation. In this paper, a road geometry estimation method is proposed without requirement of accurate positioning and lane information. Due to the fact the lane guides the front vehicle, the potential correlation between the relative azimuth of front vehicle and the direction of road is explored. The stable and observable information of front vehicles are exploited, such as the license plate location. A radar-camera fusion system using image processing, and a nonlinear system is then built with Unscented Kalman Filter to estimate the road curvature. Simulation results with Carla demonstrate that the method enhances the road geometry perception by 77.1%, compared with the baseline method. The proposed method can provide reliable road geometry information for autonomous driving when accurate positioning and lane information are unavailable, so that it renders more robust and safer perception for future autonomous driving.

Estimating mountainous freeway crash rate: Application of a spatial model with three-dimensional (3D) alignment parameters

The road alignment is a three-dimensional (3D) curve in nature. In this study, we quantitatively examine the effect of 3D road alignment on traffic safety on mountainous freeways. Geometric parameters of 3D curvature and torsion in mathematics are derived to characterize the 3D road curve. Based on the coordination of different horizontal and vertical elements, 3D road alignment is divided into twelve types of combined alignment. For each alignment combination, the 3D curvature and torsion are calculated according to the differential geometry theory. Regarding crash statistical modeling, the Bayesian spatial Tobit (BST) model is developed to accommodate possible spatial correlation of traffic crashes among adjacent freeway segments. The Bayesian Tobit (BT) model is also built for comparison.A 118-km mountainous freeway associated road geometric features, traffic volume with three years of crash data is used as a case study. The result from the model comparison shows the BST model outperforms the BT model in terms of goodness-of-fit. Parameter estimation result for the BST model shows that the differences of average 3D curvature (and torsion) between adjacent segments have statistically significant effects on the crash rate of the segment, indicating it is necessary to consider three-dimensional alignment parameters in estimating mountainous freeway crash rate. Moreover, by comparing the predicted crash rate calculated by the BST model and the observed crash rate, the result shows the proposed BST model can provide a reliable prediction for freeway crash rates of different combined alignments. This study provides new insight on the effect of road geometric design on traffic safety but also deepens our understanding of spatial correlations in freeway crash modeling.

Efficient Message Dissemination on Curve Road in Vehicular Networks

Effective emergency message dissemination is a great importance on a specific road in vehicular networks (VN). The existing methods are not most efficient solutions for message dissemination on the curve road, which primarily focus on highway and urban road. In order to improve the efficiency of message dissemination on the curved road, the paper proposed a message dissemination method based on bidirectional relay nodes. The message can be disseminated in two directions simultaneously. The paper designed a relay node selection method based on the neighbor nodes’ coverage length of the road. Different waiting delays are assigned to the neighbor nodes according to the cover capability of the road in which the message has not arrived. Simulation results demonstrated that the efficiency of the proposed method is superior to the common solutions in terms of the contention delay and the propagation velocity.

The status of all types of rollover crashes in Northern Iran and the contributing factors

IntroductionThere is little information about this type of traffic accident in northern Iran.AimThis study aimed to determine the status of rollover crashes and the factors relating them.Material and methodsThis was a retrospective analytical study. The data of this study were obtained from the reports of Traffic Police and the Department of Forensic Medicine. The data of this study included related data on vehicle damages, injuries, and fatalities of 5999 rollover crashes of motor and non-motor vehicles. All analyses were performed with Excel and SPSS v. 21 software.Results and discussionThe probability of mortality due to rollover crashes decreases on slippery (<i>P</i> < 0.001) with age (<i>P</i> < 0.001) and at night (<i>P</i> < 0.001) and increases on road curves (<i>P</i> = 0.005) . The probability of injuries reduced with increase with age (<i>P</i> < 0.000) and in men (<i>P</i> < 0.027), on slippery roads (<i>P</i> < 0.04), heavy vehicle (<i>P</i> < 0.001), car (<i>P</i> < 0.009), pickup trucks (<i>P</i> = 0.011), but increased on the road curves (<i>P</i> < 0.001).ConclusionsCountermeasures include training drivers to increase their ability to control the vehicle and pay attention to the front and avoid driving in the opposite direction at the speed limit. Moreover, installing lower speed limits and installing speed cameras in high-risk areas reduce crashes. Corrective changes to the road and reduce road curve sharpness are as effective as trying to change drivers' behavior.

Path tracking of autonomous vehicle with consideration of vehicle stability in sideslip angle phase plane

In current research of autonomous vehicle path tracking, most contributions focus on tracking accuracy during complex driving condition. However, the vehicle might fall into unstable dangerous condition when the road curvature is big or with high speed. A path tracking control framework is proposed in this paper, which considers path tracking accuracy and vehicle stability. In general, the control framework consists of three main components: vehicle stability judging controller, path tracking controller, and torque distributor. Firstly, the stability of the current vehicle can be evaluated by the stability judgment controller according to the phase plane of the sideslip angle. Then, the proportion of vehicle stability in the control objective is adjusted according to the current vehicle stability. To improve the path tracking accuracy, the path tracking is composed of a combined controller using MPC algorithm, which controls the front wheel angle and direct yaw moment at the same time. After that, the torque distributor is developed to distribute the desired yaw moment into four executive wheels, which synthesizes the results of power performance distribution and stability distribution. Finally, the effectiveness of the proposed control framework is verified by the Matlab-CarSim co-simulation experiment and the hardware-in-the-loop experiment based on LabVIEW-RT. The results show that the proposed controller has better tracking accuracy and stability than the general MPC controller.

Intelligent vehicle lateral control based on radial basis function neural network sliding mode controller

Based on the predigestion of the dynamic model of the intelligent firefighting vehicle, a linear 2-DOF lateral dynamic model and a preview error model are established. To solve the problems of a highly non-linear vehicle model, time-varying parameters, output chattering, and poor robustness, the Radial Basis Function neural network sliding mode controller is designed. Then, different driving speeds are used to conduct simulation tests under standard double-shifting and smooth curve road conditions, and the simulation results are used to analyse the tracking effect of the lateral motion controller on the desired path. The simulation results reveal that the controller designed has high accuracy in tracking the desired path and has good robustness to the disturbance of intelligent firefighting vehicle speed changes.

Multi-objective H2/H∞ saturated non-PDC static output feedback control for path tracking of autonomous vehicle

This paper presents a new fuzzy output feedback control design for autonomous vehicle steering under actuator saturation, unavailability of the sideslip angle measurement, unknown road curvature, and lateral wind force. To take into account the actuator constraint, the saturation effect is transformed into dead-zone nonlinearity. A static output controller based on non-compensation parallel distributed technic and a Takagi-Sugeno (T-S) model of vehicle lateral dynamics is proposed to consider the unavailability of some vehicle states. To avoid the problem of imposing bounds on membership functions time derivatives resulting from the use of the fuzzy Lyapunov approach, a proper integral structure based on the non-quadratic Lyapunov approach is investigated. The mixed [Formula: see text] stabilization conditions of the augmented closed-loop system are expressed in terms of linear matrix inequalities (LMIs). Finally, the robustness and the advantages of the proposed approaches are demonstrated through different tests.