Analysis Of Vehicle Behavior Research Articles

Analysis of vehicle behavior under crosswind using air spring pressure

Analysis of vehicle behavior under crosswind using air spring pressure

THE ROLE OF TOPOLOGY IN HIGH-DEFINITION MAPS FOR AUTONOMOUS DRIVING

Abstract. Autonomous and highly automated driving has become one of the key research topics – even in mapping sciences. Today, it has been clearly proven that the desired autonomy can be reached solely by progressive development, where maps and requirements against environmental models shall be modified. The research of the past decades resulted in the specifications for building high-definition (HD) maps, which contain all necessary field objects with their relevant features in a sophistically designed database. Maps have also become indispensable tools in reliable automotive development processes as simulations request accurate and detailed environment descriptions. The most accepted simulator map format is ASAM’s OpenDRIVE, having a complete ecosystem nowadays with ultra-fine resolution pavement surface model, traffic flow description, and essential modules to produce those components by mainly automatic data collection and processing. Simulations enable efficient analysis of vehicle behavior as well as testing workflow for (onboard) vehicular and infrastructure sensors. With respect to the requirements of HD maps, not only their geometric fidelity but also the correct topology is expected. The available technology already serves OpenDRIVE models for various scenarios, where the topologic correctness is hard to test. The current research puts the emphasis on the topology analysis: with parsing the existing models, topology descriptors are derived, then a test suit containing rules of acceptable cases is applied. The rule set is built of items for detecting errors and warnings considering topology parameter tolerances; the result is quality documentation after a comprehensive testing approach. The actual topology testing environment forms an excellent base for (semi)automatic error fixing platforms involving artificial intelligence.

Study and Evaluation of Wireless Sensor Networks Performance

This project analyses vehicle behavior from measurements of air pollutants (CO2) sound pollution levels, measured in ppm and decibels respectively. Such measurements are taken by a wireless sensor network mounted in the Baghdad Dora Express way, Iraq. Four sensor nodes and a coordinator form the wireless network based on the Zig bee norm. The sensor nodes wirelessly send the data to the coordinator, who displays the actions defined by the measurement curves through a graphic interface built in JAVA and store the data on a MySQL web server. The analysis of vehicle behavior considers noise levels and carbon dioxide concentration higher than their base reference value, measured in a certain period of time. Comparative and individual graphs of noise and carbon dioxide concentration taken in two days of measurement, belonging to the nodes, show greater vehicular activity in peak hours that are reduced at the end of the day and at midnight, in addition to the individual performance of the nodes within the network, which depends on the infrastructure of the Integrated Security System ECU911.

Comparative analysis of vehicle behavior for different signal lamp positions at complex intersection

Comparative analysis of vehicle behavior for different signal lamp positions at complex intersection

Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin

Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field, mainly due to the wide variety of anomaly cases and the complexity of surveillance videos. In this study, a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed. First, detecting vehicles based on deep learning is implemented, and Kalman filtering and feature matching are used to track vehicles. Subsequently, the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine, and each vehicle’s behavior is tested according to the customized detection conditions set up in the scene. The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis. The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems. In addition, the implementation and analysis process show the usability, generalization, and effectiveness of the proposed framework.

Coupling analysis of unsteady aerodynamics and vehicle behavior with road input: Modeling and verification in road tests

Coupling analysis of unsteady aerodynamics and vehicle behavior with road input: Modeling and verification in road tests

Expandable-Partially Observable Markov Decision-Process Framework for Modeling and Analysis of Autonomous Vehicle Behavior

When modeling decision-making in autonomous vehicles (AVs), only a fraction of the required information is usually available at the start due to the presence of uncertainty in the knowledge of the state of the system and the environment. This article presents a probabilistic modeling framework based on a combination of partially observable Markov decision-process (POMDP) models and heuristics. In this article, POMDPs are expanded and redefined as expandable-POMDPs to account for uncertainty and unexplainable observations. Specifically, this approach incorporates new hidden states in the model and expands associated probability distributions in the model to account for partial knowledge and unexplainable observations. An online policy estimation algorithm is presented. This algorithm employs heuristic search combined with distance-based clustering to estimate long-term rewards in a finite-horizon look-ahead scheme for expandable-POMDPs. Specifically, heuristics are employed in model design and online policy evaluation to circumvent state-space explosion and reduce high computational complexity. Finally, the expandable-POMDP models are employed for designing multiple context-specific models for tactical planning and decision-making by AVs in simulated scenarios characterized by partial observability and uncertainty. One (or more) expandable-POMDP model is invoked when the contextual pattern of the ongoing scenario fits the objective of the tactical planning model.

Pretraining Convolutional Neural Networks for Image-Based Vehicle Classification

Vehicle detection and classification are very important for analysis of vehicle behavior in intelligent transportation system, urban computing, etc. In this paper, an approach based on convolutional neural networks (CNNs) has been applied for vehicle classification. In order to achieve a more accurate classification, we removed the unrelated background as much as possible based on a trained object detection model. In addition, an unsupervised pretraining approach has been introduced to better initialize CNNs parameters to enhance the classification performance. Through the data enhancement on manual labeled images, we got 2000 labeled images in each category of motorcycle, transporter, passenger, and others, with 1400 samples for training and 600 samples for testing. Then, we got 17395 unlabeled images for layer-wise unsupervised pretraining convolutional layers. A remarkable accuracy of 93.50% is obtained, demonstrating the high classification potential of our approach.

Safety analysis of a vehicle equipped with Cooperative Adaptive Cruise Control

Stability and string stability of platoons equipped with Cooperative Adaptive Cruise Control (CACC) is widely discussed in literature. However, safe behavior of a vehicle in a platoon, i.e., making sure that a vehicle is not involved in a collision with its preceding vehicle, is not considered to a large extent. This paper exploits techniques from invariant set theory to perform a safety analysis of vehicle behavior in a platoon. Here, regions in the state space are determined that guarantee safe behavior of a vehicle for all future time. These so-called safe sets are determined for CACC under various circumstances, in particular with and without wireless inter-vehicle communication. The proposed safety analysis framework supports real-time safety monitoring in vehicles.

Analysis of Abnormal Vehicle Behavior Based on Trajectory Fitting

In order to analysis the abnormal vehicle behavior by trajectory fitting effectively, the whole process is divided into three steps: target detection and tracking, vehicle trajectory analysis, vehicle behavior detection. Firstly, a three-frame-differencing method is used to achieve initial target location and an improved tracking algorithm based on Kalman predictor is proposed; then, an adaptive segmented linear fitting algorithm is proposed to achieve vehicle trajectory fitting; finally, two parameters including the rate of velocity variation and the rate of direction variation are used to establish vehicle abnormal behavior detection model. Experiment result shows that the three high dangerous vehicle behaviors in road surveillance videos can be detected effectively: sharp brake, sharp turn, and sharp turn brake.

Modeling and analysis of vehicle behavior at bottlenecks

Microscopic traffic models are developed to simulate traffic phenomena. However, current models are usually based on the fact that vehicles travel in the middle of a single lane and make a lateral movement only when the driver wants to change lane. They are incapable of describing driving behavior in a complex traffic environment with a lot of lateral separations which may cause traffic flow to be unstable on a single lane. In order to solve the problem, in this paper we first build up the equations of motion based on target steering angle (TSA) and then establish a series of rules for determining TSA by considering the lateral separation characteristics between the follower and the leader. Finally a new vehicular behavior model is developed. The properties of the model are investigated by simulating two scenarios. It is found that the proposed model is capable of describing complex phenomena which cannot be described by existing models and explaining the perturbation caused by bus near bus stations. The results also imply that this model with taking the lateral separation characteristic into account greatly enhances the effectiveness of vehicular behavior.

Guiding Model and Empirical Analysis of Vehicle Behavior at Signalized Intersections Based on Carbon Emissions

A model based on carbon emissions is established to guide vehicle behavior at signalized intersections. This model takes working conditions and emission characteristics of vehicles into account, uses VT-Micro model exploited by Oak Ridge National Laboratory to calculate emissions, and provides vehicles with the lowest-emission working plan to drive through intersections. This model is able to reducing carbon emissions at the source and it agrees with the development of low-carbon transportation.

An Analysis of Vehicles' Behavior in Approaching to Small Intersections Aiming at Distinction of Stop Failure Vehicle from Road Side

This study proposed distcriminant models which can recognize dangerous approaching vehicles by using their position and speed which can be measured from road side sensors. It aims to develop road side warning facilities for collision avoidance at small unsignalized junctions. According to the analysis of vehicle behavior approaching to junctions, it is found that 90% of stopping vehicles begin their braking during 30m and 10m from the junction, and the beginning point is influence by the width of street on which they are moving. From results of discriminant analysis between dangerous approaching vehicles and safe approaching vehicles using speed and change of speed variables as explanation indices, it is clarified that around 90% of samples would be distinguished correctly and appropriate sensing areas referring to street's width were found.

Analysis of Roll and Lateral Vehicle Behavior by Road Input

Analysis of Roll and Lateral Vehicle Behavior by Road Input

2209 マルチボディ車両モデルによる不整路面走行時の車両挙動解析(シミュレーション技術,OS3 交通・物流システムのダイナミクスと制御)

This paper describes the analysis of vehicle behavior using a multibody vehicle model. The irregular road is represented by a three-dimensional road model. The three-dimensional road model can create the various road roughnesses with some parameters. The multibody vehicle model in this study is modelled from the actual vehicle. In this simulation, the tire vertical force and vehicle behavior such as roll angle, pitch angle and body displacement are analyzed. To consider the road shape in the analysis of vehicle behavior, a method for calculating the tire force is proposed. In the method, a tire is expressed by some planes and the tire force is calculated when the plane is contacted with road and the tire force is decomposed to longitudinal force and lateral force and vertical force.

地震に対する鉄道車両の走行安全性解析(<メカライフ特集>安全・安心)

地震に対する鉄道車両の走行安全性解析(<メカライフ特集>安全・安心)

Closed-loop analysis of vehicle behavior during braking in a turn

This paper discusses an analysis of “driver-vehicle” behavior during braking in a turn. In this study, a new braking model was developed in consideration of actual driver's operation, and experiments were conducted on mountain roads to identify one novice and one skilled driver's braking model parameters. The computer simulations have shown the following tendency. The novice driver needed more compensatory steering operation than the skilled driver to trace the course. A controlled braking force distribution was particularly effective for the novice driver.

Analysis of Vehicle Behavior at a Merging Section of Personal Rapid Transit with Two Entry Points

Analysis of Vehicle Behavior at a Merging Section of Personal Rapid Transit with Two Entry Points

Analysis of vehicle behavior at a merging section of APM system with different strategies

This paper considers the merging section of an APM (automated people mover), which is an approach to the new traffic system. The behavior of the vehicles at the merging section is modeled, and the situation, when different strategies are adopted, is analyzed using the technique of Markov chain. An APM system is considered, where the vehicles in the system do not always run individually, but sometimes run in the form of a train. This paper attempts to point out the essential dependencies of the vehicle behavior on the control strategy. It is assumed that the vehicles arrive individually. The basic idea of the control strategy is that the individually incoming vehicles are connected at the merging section. Thus the vehicle capacity of the outgoing line from the merging section is equivalently increased. As the result of analysis, the relations among the arriving traffic of the vehicles in two incoming lines, the average waiting time of the vehicles in the individual lines, the average number of connected vehicles in a train, and the density of train operations are derived explicitly. The dependencies of the average waiting time of the vehicle and the outflow traffic density of the train on different control strategies are examined.

Analysis of Re-Entry Vehicle Behavior during Boundary-Layer Transition

Analysis of re-entry vehicle (RV) behavior during the period of boundary-laye r transition is performed using the full three degree of rotational freedom plane fixed equations of motion without limitations of linearization s or quasi-steady restrictions. Flight data for a number of RVs, including ablative and nonablative afterbodies, showed large divergencies in the magnitude and sense of the precession characteristics. Associated with the large excursions in the precession behavior are significant changes in the character of the motion, i.e., the ratio of maximum to minimum angle of attack. Three possible sources of these anomalies were investigated: unstable damping;- static out-of-plane moments; in-plane static moment asymmetries. Analyses show the unstable damping postulation is highly improbable; simulations employing out-of-plane static moments adequately describe the majority of flight results. However, significant discrepancies are noted for two flights. A rapidly varying body fixed moment source is described which provides reasonable simulations of the precession divergencies and highly transient angle of attack behavior.