Vehicle Front Research Articles

COOPERATIVE LOCALISATION USING IMAGE SENSORS IN A DYNAMIC TRAFFIC SCENARIO

Abstract. Localisation is one of the key elements in navigation. Especially due to the development in automated driving, precise and reliable localisation becomes essential. In this paper, we report on different cooperation approaches in visual localisation with two vehicles driving in a convoy formation. Each vehicle is equipped with a multi-sensor platform consisting of front-facing stereo cameras and a global navigation satellite system (GNSS) receiver. In the first approach, the GNSS signals are used as excentric observations for the projection centres of the cameras in a bundle adjustment, whereas the second approach uses markers on the front vehicle as dynamic ground control points (GCPs). As the platforms are moving and data acquisition is not synchronised, we use time dependent platform poses. These time dependent poses are represented by trajectories consisting of multiple 6 Degree of Freedom (DoF) anchor points between which linear interpolation takes place. In order to investigate the developed approach experimentally, in particular the potential of dynamic GCPs, we captured data using two platforms driving on a public road at normal speed. As a baseline, we determine the localisation parameters of one platform using only data of that platform. We then compute a solution based on image and GNSS data from both platforms. In a third scenario, the front platform is used as a dynamic GCP which can be related to the trailing platform by markers observed in the images acquired by the latter. We show that both cooperative approaches lead to significant improvements in the precision of the poses of the anchor points after bundle adjustment compared to the baseline. The improvement achieved due to the inclusion of dynamic GCPs is somewhat smaller than the one due to relating the platforms by tie points. Finally, we show that for an individual vehicle, the use of dynamic GCPs can compensate for the lack of GNSS data.

Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method.

Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated.

Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

Smart traffic control: Identifying driving-violations using fog devices with vehicular cameras in smart cities

Growing vehicular traffic in urban areas creates a mess for authorities to handle city traffic. With the lack of human resources, authorities are moving towards the use of smart and auto-traffic control systems to manage an increasing volume of traffic. Mostly, these systems monitor traffic using street cameras and identify illegal traffic behaviors, such as signal violations. However, it is not feasible to employ humans or static cameras everywhere in the city in order to cover all the urban roads. These days, modern automobiles come with cameras to store videos as a black-box in case of an accident. In this paper, we exploited the use of vehicular cameras and proposed a smart traffic control model to report any traffic violation on the road. To this end, the vehicle's camera monitors all front cars on the road and transmits videos to the car's attached fog device. The fog device analyses the captured video for unlawful behavior and reports to traffic authorities in case of any violation. Initially, front vehicles are recognized using Single Shot MultiBox Detector (SSD), whereas road lanes are marked using Hough transform. Later, the violations are identified using the violation-detection algorithm. As a use case, the algorithm is designed for the fog device to identify driving violations, including wrong U-turn and driving on a central divider line or a yellow line. The role of the fog device is implemented on a GTX750-Ti GPU-based machine. Finally, the system's performance is evaluated in terms of accuracy and efficiency.

CNN‐based estimation of heading direction of vehicle using automotive radar sensor

Modern autonomous vehicles are being equipped with various automotive sensors to perform special functions. Especially, it is important to predict the heading direction of the front vehicle to adjust the speed of the ego-vehicle and select appropriate actions. Here, we propose a method for estimating the instantaneous heading direction of a vehicle using automotive radar sensor data. First, using a frequency-modulated continuous wave (FMCW) radar in the 77 GHz band, we accumulate the automotive radar sensor data for different movements of the front vehicle (e.g., stop, going ahead, reversing, turning left, and turning right). To distinguish the different movements of the vehicle, we use the convolutional neural network (CNN) and train it using the acquired radar sensor data. Because the CNN algorithm usually uses image data as input, it is essential to convert radar sensor data into image data. Therefore, we apply a high-resolution angle estimation algorithm to the obtained radar data and convert it into a two-dimensional range map. After the CNN model is trained with the obtained radar sensor data, various movements of the front vehicle can be classified with over 94% of accuracy.

Forecasting Short to Mid-Length Speed Trajectories of Preceding Vehicle Using V2X Connectivity for Eco-Driving of Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory. The simulation results show that the proposed predictor’s improved predictions can reduce energy consumption by 5% over eco-driving with the baseline predictor.</div></div>

Biomechanical Responses and Injury Assessment of Post Mortem Human Subjects in Various Rear-facing Seating Configurations.

The objective of this study was to generate biomechanical corridors from post-mortem human subjects (PMHS) in two different seatback recline angles in 56 km/h sled tests simulating a rear-facing occupant during a frontal vehicle impact. PMHS were placed in a production seat which included an integrated seat belt. To achieve a repeatable configuration, the seat was rigidized in the rearward direction using a reinforcing frame that allowed for adjustability in both seatback recline angle and head restraint position. The frame contained instrumentation to measure occupant loads applied to the head restraint and seatback. To measure PMHS kinematics, the head, spine, pelvis, and lower extremities were instrumented with accelerometers and angular rate sensors. Strain gages were attached to anterior and posterior aspects of the ribs, as well as the mid-shaft of the femora and tibiae, to determine fracture timing. A chestband was installed at the mid sternum to quantify chest deformation. Biomechanical corridors for each body and seat location were generated for each recline angle to provide data for quantitatively evaluating the biofidelity of ATDs and HBMs. Injuries included upper extremity injuries, rib fractures, pelvis fractures, and lower extremity injuries. More injuries were documented in the 45-degree recline case than in the 25-degree recline case. These injuries are likely due to the excessive ramping up and corresponding kinematics of the PMHS. Biomechanical corridors and injury information presented in this study could guide the design of HBMs and ATDs in rigid, reclined, rear-facing seating configurations during a high-speed frontal impact.

Online optimum velocity calculation under V2X for smart new energy vehicles

In this paper, a vector data net solver is proposed, which can reduce bivariate discrete-time dynamic programming (DP) computation time by 98.0% without losing accuracy. Therefore, for the first time, bivariate discrete-time DP can operate under model predictive control rolling optimization to calculate future optimum vehicle velocity in real time considering future road altitude and instant traffic information. Simulation results indicate that with the solution presented in this paper, front vehicles and the proper windows to pass through front intersections can be constantly considered. Meanwhile, the calculated optimum vehicle velocity almost remains the same as the global optimum solutions. Simulation results are validated by real-car tests, and the test new energy vehicle (NEV) electricity consumption is reduced by up to 48.6%. A comparison experiment is performed between the solution presented in this paper and commonly used adaptive dynamic programming (ADP), and the results indicate that the former has better performance and stability. This paper describes a novel solution for online optimum velocity calculation under vehicle to everything (V2X) environment and can be used by all smart NEVs with autonomous driving or active cruise control functions for lower electricity consumption and better riding comfort.

Arduino Based automatic Vehicle Control

The objective of this research is to develop a system to keep the vehicle secure and protect it by the occupation of the intruders. The aim is automatically controlling the speed of vehicle and accident avoidance using arduino and ultrasonic sensors. Arduino mega 2560 is used to create the path through programing. The programed path is fed into the Arduino which is followed by the vehicle. Whenever any obstacle is detected by the running vehicle, depends on distance automatically control the speed of vehicle. The ultrasonic sensor system continuously sends signals and monitors any car or other obstacles are in front of vehicle. The distance up to which ultrasonic sensor can work may be up to 4 m. When any obstacle or vehicle is detected by ultrasonic sensor system, it will send signal to the embedded board. After receiving this signal embedded board sends a signal to the motor to reduce the vehicle speed automatically which can control vehicle speed immediately. Vehicle is controlled automatically without any manual operation when the obstacle is at 4 m distance away from the front vehicle. In critical cases, if vehicle is met into accident GSM send a message to the user.

Preceding Vehicle Detection Based on Optimized Faster R-CNN Algorithm

Preceding vehicle detection is still a challenge for unmanned driving technology. Deep learning has achieved great success in target detection. Among them, the Faster R-CNN algorithm is a classic representative. However, the algorithm still has some room for improvement in detection accuracy. By analyzing the problems of Faster R-CNN in the detection of occluded vehicles, taking the target detection post-processing algorithm Soft-NMS as the research object, two new penalty coefficients, inverse proportional penalty coefficient and exponential penalty coefficient, were proposed. It further improves the algorithm’s detection accuracy of the blocked vehicle in front.

Evolution of requirements to state of order of automotive tires for traffic safety

The paper presents the results of a study of the influence of the amendment of the requirements to the technical state of order of motor vehicles, specifically, to the design of the tires used on the front and rear axles of motor vehicles on the motor traffic safety. It has been determined that newly developed requirements aim on the reduction of the difference of the traction between the front and the rear axle for more prompt response of the braking mechanisms and improved braking efficiency. It has been demonstrated that the implementation of the design requirements to vehicles developed in 2010 an 2013 allows for reduction of the accident rates of motor vehicles using different tire types by 3%. Further tightening of the operational requirements to motor vehicles in this direction has a reserve allowing for max. 1% of further reduction of the accident rate whereafter the reserve of the traffic safety improvement will be deemed exhausted.

Research on the Cascade Vehicle Detection Method Based on CNN

This paper introduces an adaptive method for detecting front vehicles under complex weather conditions. In the field of vehicle detection from images extracted by cameras installed in vehicles, backgrounds with complicated weather, such as rainy and snowy days, increase the difficulty of target detection. In order to improve the accuracy and robustness of vehicle detection in front of driverless cars, a cascade vehicle detection method combining multifeature fusion and convolutional neural network (CNN) is proposed in this paper. Firstly, local binary patterns, Haar-like and orientation gradient histogram features from the front vehicle are extracted, then principal-component-analysis dimension reduction and serial-fusion processing are performed on the input image. Furthermore, a preliminary screening is conducted as the input of a support vector machine classifier based on the acquired fusion features, and the CNN model is employed to validate cascade detection of the filtered results. Finally, an integrated data set extracted from BDD, Udacity, and other data sets is utilized to test the method proposed. The recall rate is 98.69%, which is better than the traditional feature algorithm, and the recall rate of 97.32% in a complex driving environment indicates that the algorithm possesses good robustness.

Determining vehicle pre-crash speed in frontal barrier crashes using genetic algorithm model adjustment techniques for intermediate car class

This paper introduces another approach to pre-crash velocity determination by the means of genetic algorithm model adjustment (GAMA). This innovative method is based on the standard genetic algorithm approach applied to the problem of determining the dependency of predicted variable on given parameters. The experiment is performed on a database obtained from NHTSA (National Highway Traffic Safety Administration), which contains potent number of data samples regarding frontal vehicle crash tests: i.e. vehicle mass, deformation zone and deformation coefficients C1-C6. Various classes of possible functions are considered and the model for determining the pre-crash speed based on the given parameters is obtained.

Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment

The prediction of lane change intention of vehicles is an important part of the decision planning and control systems of intelligent vehicles. In the dynamic and complex traffic environment, the behaviors of traffic participants interact and influence each other. In lane change prediction, it is necessary to study the predicted vehicle and surrounding vehicles as an interactive correlation system. Otherwise, great errors are made in the motion prediction. Based on this, the motion state of the predicted vehicle, the position relationship between the predicted vehicle and lane, as well as the motion state of vehicles around the predicted vehicle are considered systematically in this paper, and the prediction of lane change intention of vehicles is studied. The influence of the three above-mentioned factors on the prediction of lane change intention is analyzed in this paper. On the basis of screening the prediction features of lane change intention, the lane change intention of vehicles is predicted by a feed-forward neural network. The data collected by the virtual driving experiment platform are divided into a training set, a verification set, and a test set. The neural network parameters of vehicles’ lane change intentions are identified by a training set, and the effect of prediction is tested by a verification set and a test set. The results show that the accuracy of the prediction model is high. The model is compared with the model of common features at the present stage and the model based on a Support Vector Machine, and the results show that the accuracy of the prediction model proposed in this paper was improved by 6.4% and 2.8%, respectively, compared with the two models. Finally, the virtual driving experiment platform was used to predict the lane change intention of the front vehicle and the vehicle in the left adjacent lane. The results show that, based on the same model and input features, the lane change intention of the front vehicle and the vehicle in the left adjacent lane can be predicted by the model at 2.8 s and 3.4 s before the lane change, and the model is a certain generality for the prediction of lane change intention of adjacent vehicles.

A basic design for automotive crash boxes using an efficient corrugated conical tube

Frontal vehicle structure is of high importance through crash energy managements and crash boxes are the fundamental structural component for vehicle safety as well as after sales issues. Similar to many other vehicle components, the detail design of crash box is usually part of manufacture knowhow. However, some guide lines are always available. In this article a general procedure is introduced for designing of crash box with the aid of novel thin walled structures and according to conventional crash scenarios. The problem is followed through some basic steps. Firstly, the crash box idea is selected through a wide range of previous investigated elements and is packaged in a real bench vehicle. Then thanks to the protection provided by the new crash box on the other more expensive components (e.g. headlamp, cooling pack, etc.), the effectiveness of this element are acknowledged through the low speed offset crash. Further on the robustness of new proposed crash box is approved by high speed crash simulations. The quasi-static simulations implemented during the analyses are carried out by finite element explicit code (Abaqus) and the FE modeling and dynamic simulation through the next steps are also performed in ANSA and PAM CRASH respectively. Finally in addition to the general crash box design proposed procedure, the achieved results demonstrated that the corrugated conical thin walled tubes deforms in regular and rather stable shape under both axial and oblique loadings. They also produced a reasonable reaction force versus deformations which leads to stiff and crashworthy energy absorber in comparison to traditional rectangular and even some special models like as origami shapes, and so they could be a valuable selection for crash box implementations in passenger cars.

MVCM Car-Following Model for Connected Vehicles and Simulation-Based Traffic Analysis in Mixed Traffic Flow

Although there have been assorted car-following (CF) models for connected vehicles (CVs), studying their impacts in mixed traffic flow of human-driven vehicles (HVs) and CVs remains a challenge. Considering the multiple front vehicles’ optimal speed changes with memory, this study proposed a new CF model (MVCM model) implemented through vehicle-to-everything (V2X) technology in CVs environment in terms of OVCM (Optimal Velocity Changes with Driving Memory) model. The stability condition of MVCM model was derived through linear stability analysis. Then, the disturbance propagation of MVCM model was compared with that of both classical FVD (Full Velocity Difference) model and MHOVA (Multiple Headway Optimal Velocity and Acceleration) model. Finally, a case study was conducted in VISSIM to analyze the impact of CV (with MVCM model) rates on traffic characteristics, including the average speed, delay time and travel time. Results show that 1) considering more front vehicles’ optimal speed strengthens the stability of traffic flow and the optimal considered vehicle number is 4; 2) MVCM model shows better resistance to disturbance than FVD model; 3) and obtain the same stability with MHOVA model, but with fewer front vehicles considered (only 3); 4) larger rate of CVs leads to higher average speed, smaller average travel time and the delay time of CVs and HVs; and 5) CVs’ positive effects reaches stable when CV rate approaches 0.6.

Velocity Optimization of Pure Electric Vehicles with Traffic Dynamics and Driving Safety Considerations

As Electric Vehicles (EVs) become increasingly popular, their battery-related problems (e.g., short driving range and heavy battery weight) must be resolved as soon as possible. Velocity optimization of EVs to minimize energy consumption in driving is an effective alternative to handle these problems. However, previous velocity optimization methods assume that vehicles will pass through traffic lights immediately at green traffic signals. Actually, a vehicle may still experience a delay to pass a green traffic light due to a vehicle waiting queue in front of the traffic light. Also, as velocity optimization is for individual vehicles, previous methods cannot avoid rear-end collisions. That is, a vehicle following its optimal velocity profile may experience rear-end collisions with its frontal vehicle on the road. In this article, for the first time, we propose a velocity optimization system that enables EVs to immediately pass green traffic lights without delay and to avoid rear-end collisions to ensure driving safety when EVs follow optimal velocity profiles on the road. We collected real driving data on road sections of US-25 highway (with two driving lanes in each direction and relatively low traffic volume) to conduct extensive trace-driven simulation studies. Results show that our velocity optimization system reduces energy consumption by up to 17.5% compared with real driving patterns without increasing trip time. Also, it helps EVs to avoid possible collisions compared with existing collision avoidance methods.

Study on the Effect of Stiffness Matching of Anti-Roll Bar in Front and Rear of Vehicle on the Handling Stability

In order to study the effect of front and rear anti-roll bar’s stiffness on vehicle’s handling stability, the structure and force of the anti-roll bar are analysed, and the vehicle’s rolling model, simplified car motion model and multi-body dynamics model of a car are established. The influence of stiffness change of the anti-roll bar on understeer of the vehicle is studied by constant radius cornering simulation. In swept-sine steering input simulation, the influence of stiffness change of the anti-roll bar on the response indexes of swept-sine steering input simulation are studied, the rule of these indexes changing with stiffness of anti-roll bar is obtained. It is found at high frequencies, due to the damping effect of the system, the influence of stiffness change on yaw rate gain is opposite to that of low frequency. In step steer input simulation, the influence of stiffness change of anti-roll bar on lateral acceleration and transition time are studied. Finally, this paper matches the stiffness of front and rear anti-roll bars of the vehicle based on swept-sine steering input simulation. The results show that the handling stability of the vehicle becomes relatively better after the stiffness of the anti-roll bars is matched.

A New Method for Static Measurement of Two Axles Vehicle Centroid Height

Background: Vehicle centroid height is an important parameter in vehicle control systems. It plays an important role in vehicle roll stability and handling stability. At present, there are mainly static and dynamic measurement methods such as swing method, mass reaction method, marking method and so on. These static measurement methods often require a fixed suspension to make the measurement process time-consuming and laborious, and measurement errors will occur when the suspension is not fixed firmly, and it is impossible to measure the mass center position of spring-loaded mass and non-spring-loaded mass. Objective: Therefore, it is of great practical significance to study a high accuracy, low cost and fast measurement method of vehicle centroid height. We revised and summarized all patents relating to methods for estimating vehicle centroid height. And a new method of centroid measurement is designed in this paper. Methods: According to the change of tire flattening radius and suspension, the change of spring and non-spring mass center height when vehicle axle load changes, the method of delineation is used to calculate the change of tire flattening radius and suspension; According to the relationship between vehicle tilt angle and front and rear axle load, the principle of moment balance and the principle of mass balance, it determines the geometric relationship between the variables, and to take the moment of the contact point between the front wheel and the ground, the moment balance equation is established, and the height of the vehicle mass center, spring load and non-spring load mass center in the horizontal position of the vehicle are obtained. Results: This measurement method is different from the existing static measurement of vehicle centroid height, it does not need the fixed suspension, only need to measure several dimensions, we can avoid heavy labor and safety accidents brought by the fixed suspension, it can also reduce the measurement error caused by fixation, but also can draw the centroid location of the sprung mass and unsprung mass. The measurement method is simple, economical and accurate, and is widely applicable on two-axle vehicles. Conclusion: The purpose of this paper is to provide a static and accurate measurement method of vehicle centroid height, so as to solve the problem that the current static measurement method of vehicle centroid height needs fixed suspension, and measurement errors will occur when the suspension is not fixed properly, so it is impossible to measure the centroid position of spring-loaded mass and non-spring-loaded mass.

Improved fatigue failure model for reliability analysis of mechanical parts inducing stress spectrum

To study the influence of random parameter on the reliability of the vehicle front stabilizer bar, the fatigue strength is analyzed according to the random fatigue strength prediction method, then the fatigue limit of the actual parts are estimated, a new fatigue failure model is improved by using the fatigue limit [Formula: see text] of actual parts. Through this model, the stress spectrum collected by real vehicle is introduced, Monte-Carlo simulation method is adopted to analyze the reliability and sensitivities of stabilizer bar under driving conditions. In addition, the influence of each basic random parameter on the failure probability is obtained. The results show that stabilizer bar diameter has a great influence on the failure probability, which provides certain reference for improving the reliability of the front stabilizer bar.