Search

Although Cooperative Driving Automation (CDA) has attracted considerable attention in recent years, there remain numerous open challenges in this field. The gap between existing simulation platforms that mainly concentrate on single-vehicle intelligence and CDA development is one of the critical barriers, as it inhibits researchers from validating and comparing different CDA algorithms conveniently. To this end, we propose OpenCDA, a generalized framework and tool for developing and testing CDA systems. Specifically, OpenCDA is composed of three major components: a co-simulation platform with simulators of different purposes and resolutions, a full-stack cooperative driving system, and a scenario manager. Through the interactions of these three components, our framework offers a straightforward way for researchers to test different CDA algorithms at both levels of traffic and individual autonomy. More importantly, OpenCDA is highly modularized and installed with benchmark algorithms and test cases. Users can conveniently replace any default module with customized algorithms and use other default modules of the CDA platform to perform evaluations of the effectiveness of new functionalities in enhancing the overall CDA performance. An example of platooning implementation is used to illustrate the framework's capability for CDA research. The codes of OpenCDA are available in the https://github.com/ucla-mobility/OpenCDA., Accepted by ITSC2021

Document encryption and decryption ensure document security by protecting the security in data storage and transition, which prevents possible serious information leakage. Recent encryption methods tend to rely on the internet or the powerful calculation ability of computers highly. However, information is shared online and shared offline, requiring encrypting methods that are comparatively less complicated to implement, in some cases, i.e., paper or flash drives. To solve the problem, this paper presents document encryption and decryption method using the mechanism of the Enigma machine. First, we read the document and process it to leave characters only. Then, we encrypt the document into ciphertext using a given key. Finally, we decrypt the ciphertext using the key and obtain the plaintext, the original document. To verify the effectiveness of the proposed method, we encrypt documents and decrypt the ciphertext. The experimental results show that the proposed method outperforms related benchmarks.

The causes of software crashes can be hidden anywhere in the source code and development environment. When encountering software crashes, recurring bugs that are discussed on Q&A sites could provide developers with solutions to their crashing problems. However, it is difficult for developers to accurately search for relevant content on search engines, and developers have to spend a lot of manual effort to find the right solution from the returned results. In this paper, we present CraSolver, an approach that takes into account both the structural information of crash traces and the knowledge of crash-causing bugs to automatically summarize solutions from crash traces. Given a crash trace, CraSolver retrieves relevant questions from Q&A sites by combining a proposed position dependent similarity - based on the structural information of the crash trace - with an extra knowledge similarity, based on the knowledge from official documentation sites. After obtaining the answers to these questions from the Q&A site, CraSolver summarizes the final solution based on a multi-factor scoring mechanism. To evaluate our approach, we built two repositories of Java and Android exception-related questions from Stack Overflow with size of 69,478 and 33,566 questions respectively. Our user study results using 50 selected Java crash traces and 50 selected Android crash traces show that our approach significantly outperforms four baselines in terms of relevance, usefulness, and diversity. The evaluation also confirms the effectiveness of the relevant question retrieval component in our approach for crash traces.

API developers have been working hard to evolve APIs to provide more simple, powerful, and robust API libraries. Although API evolution has been studied for multiple domains, such as Web and Android development, API evolution for deep learning frameworks has not yet been studied. It is not very clear how and why APIs evolve in deep learning frameworks, and yet these are being more and more heavily used in industry. To fill this gap, we conduct a large-scale and in-depth study on the API evolution of Tensorflow 2, which is currently the most popular deep learning framework. We first extract 6,329 API changes by mining API documentation of Tensorflow 2 across multiple versions and mapping API changes into functional categories on the Tensorflow 2 framework to analyze their API evolution trends. We then investigate the key reasons for API changes by referring to multiple information sources, e.g., API documentation, commits and StackOverflow. Finally, we compare API evolution in non-deep learning projects to that of Tensorflow 2, and identify some key implications for users, researchers, and API developers.

Similar to code smells in source code, UI design has visual design smells that indicate violations of good UI design guidelines. UI design guidelines constitute design systems for a vast variety of products, platforms, and services. Following a design system, developers can avoid common design issues and pitfalls. However, a design system is often complex, involving various design dimensions and numerous UI components. Lack of concerns on GUI visual effect results in little support for detecting UI design smells that violate the design guidelines in a complex design system. In this paper, we propose an automated UI design smell detector named UIS-Hunter (UI design Smell Hunter). The tool is able to (i) automatically process UI screenshots or prototype files to detect UI design smells and generate reports, (ii) highlight the violated UI regions and list the material design guidelines that the found design smells violate, and (iii) present conformance and violation UI design examples to assist understanding. This tool consists of a Material Design guidelines gallery website and a tool website. The gallery website is a back-end knowledge base that attaches conformance and violation examples to abstract design guidelines and allows developers and designers to explore the multi-dimensional space of a complex design system in a more structured way. As a front-end application, the tool website takes a UI design as input, returns a detailed UI design smell report, and marks the violation regions (if any). Moreover, the tool website presents conformance and violation examples based on the gallery website. Demo URL: https://uishunter.net.cn/ https://uishuntergallery.net.cn/ Demo Video: https://youtu.be/7UZ0jtD_1gM

Smart contracts have been plagued by security incidents, which resulted in substantial financial losses. Given numerous research efforts in addressing the security issues of smart contracts, we wondered how software practitioners build security into smart contracts in practice. We performed a mixture of qualitative and quantitative studies with 13 interviewees and 156 survey respondents from 35 countries across six continents to understand practitioners' perceptions and practices on smart contract security. Our study uncovers practitioners' motivations and deterrents of smart contract security, as well as how security efforts and strategies fit into the development lifecycle. We also find that blockchain platforms have astatistically significant impact on practitioners' security perceptions and practices of smart contract development. Based on our findings, we highlight future research directions and provide recommendations for practitioners.

Just like code smells in source code, UI design has visual design smells. We study 93 don't-do-that guidelines in the Material Design, a complex design system created by Google. We find that these don't-guidelines go far beyond UI aesthetics, and involve seven general design dimensions (layout, typography, iconography, navigation, communication, color, and shape) and four component design aspects (anatomy, placement, behavior, and usage). Violating these guidelines results in visual design smells in UIs (or UI design smells). In a study of 60,756 UIs of 9,286 Android apps, we find that 7,497 UIs of 2,587 apps have at least one violation of some Material Design guidelines. This reveals the lack of developer training and tool support to avoid UI design smells. To fill this gap, we design an automated UI design smell detector (UIS-Hunter) that extracts and validates multi-modal UI information (component metadata, typography, iconography, color, and edge) for detecting the violation of diverse don't-guidelines in Material Design. The detection accuracy of UIS-Hunter is high (precision=0.81, recall=0.90) on the 60,756 UIs of 9,286 apps. We build a guideline gallery with real-world UI design smells that UIS-Hunter detects for developers to learn the best Material Design practices. Our user studies show that UIS-Hunter is more effective than manual detection of UI design smells, and the UI design smells that are detected by UIS-Hunter have severely negative impacts on app users.

Many automated commit message generation (CMG) approaches have been proposed for facilitating the understanding of software changes. They are shown to be promising and can generate commit messages that are semantically relevant to the reference messages for a number of commits. However, a large proportion (over 50%) of semantically irrelevant commit messages are also generated simultaneously. Such messages may mislead developers, require additional efforts of developers to confirm and filter out, and hinder the application of existing CMG approaches in practice. For tackling this problem, prior work mainly focuses on proposing new methods to improve the generation accuracy. However, another promising way for bridging the gap between CMG approaches and the practice has not been well investigated, which is: can we automatically assure the semantic relevance of the generated messages?To that end, in this work, we propose an automated Quality A ssurance framework for commit message generation (QAcom). QAcom can assure the quality of generated commit messages by automatically filtering out the semantically-irrelevant generated messages and preserving the semantically-relevant ones as many as possible. In particular, QAcom consists of a Collaborative-Filtering-based (CF) component and a Retrieval-based (RE) component. Given a commit message generated by a CMG approach, QAcom estimates whether this generated message is semantically relevant to its ground truth, which is unknown when estimating, based on both the collaborative filtering algorithm and the similarity between this commit and historical commits. We evaluate the effectiveness of QAcom by "plugging" it in three state-of-the-art CMG approaches. Experimental results on three public datasets show that QAcom can effectively filter out semantically-irrelevant generated messages and preserve semantically-relevant ones.

Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory study on the dependency networks of deep learning libraries, namely, Tensorflow, PyTorch, and Theano. We study the project purposes, application domains, dependency degrees, update behaviors and reasons as well as version distributions of deep learning projects that depend on Tensorflow, PyTorch, and Theano. Our study unveils some commonalities in various aspects (e.g., purposes, application domains, dependency degrees) of deep learning libraries and reveals some discrepancies as for the update behaviors, update reasons, and the version distributions. Our findings highlight some directions for researchers and also provide suggestions for deep learning developers and users.

In order to effectively diagnose the rub-impact fault of rotor-bearing system, this study develops an experimental model of a two-span rotor-bearing system with rubbing faults. The rubbing experiment was carried out through the experimental platform and its analysis system, and the vibration of the rotor bearing system running under normal conditions, local slight rubbing and severe rubbing throughout the cycle are observed. The Variational Mode Decomposition (VMD) is then applied to the fault data analysis experiments of different rubbing degrees of the rotor. The experimental results further verified the effectiveness of the method, which can reveal the frequency structure of rubbing faults and accurately reflect the fault information. The findings are helpful in further understanding the dynamic characteristics of the rub-impact fault of the two-span rotor-bearing system and provide reference for fault diagnosis.

In this paper, a class of three dimensional nonlinear chaotic dynamic systems, which have only one equilibrium position, are studied. The least conservative linear feedback control is introduced to make the systems globally exponentially stable. For four kinds of nonlinear chaotic systems, Sport D, I, J, L presented in reference [1], since the correlation of three equations in each system are good enough, it is relatively easy to realize linear feedback to achieve the aim of controlling chaos. Simulations are proposed to verify the good performance of the controlled systems with the given linear controllers.

Code clones are semantically similar code fragments pairs that are syntactically similar or different. Detection of code clones can help to reduce the cost of software maintenance and prevent bugs. Numerous approaches of detecting code clones have been proposed previously, but most of them focus on detecting syntactic clones and do not work well on semantic clones with different syntactic features. To detect semantic clones, researchers have tried to adopt deep learning for code clone detection to automatically learn latent semantic features from data. Especially, to leverage grammar information, several approaches used abstract syntax trees (AST) as input and achieved significant progress on code clone benchmarks in various programming languages. However, these AST-based approaches still can not fully leverage the structural information of code fragments, especially semantic information such as control flow and data flow. To leverage control and data flow information, in this paper, we build a graph representation of programs called flow-augmented abstract syntax tree (FA-AST). We construct FA-AST by augmenting original ASTs with explicit control and data flow edges. Then we apply two different types of graph neural networks (GNN) on FA-AST to measure the similarity of code pairs. As far as we have concerned, we are the first to apply graph neural networks on the domain of code clone detection. We apply our FA-AST and graph neural networks on two Java datasets: Google Code Jam and BigCloneBench. Our approach outperforms the state-of-the-art approaches on both Google Code Jam and BigCloneBench tasks.

Developers often look for solutions to programming problems in community Q&A sites like Stack Overflow. Due to the crowdsourcing nature of these Q&A sites, many user-provided answers are wrong, less optimal or out-of-date. Relying on community-curated quality indicators (e.g., accepted answer, answer vote) cannot reliably identify these answer problems. Such problematic answers are often criticized by other users. However, these critiques are not readily discoverable when reading the posts. In this paper, we consider the answers being criticized and their critique posts as controversial discussions in community Q&A sites. To help developers notice such controversial discussions and make more informed choices of appropriate solutions, we design an automatic open information extraction approach for systematically discovering and summarizing the controversies in Stack Overflow and exploiting official API documentation to assist the understanding of the discovered controversies. We apply our approach to millions of java/android-tagged Stack overflow questions and answers and discover a large scale of controversial discussions in Stack Overflow. Our manual evaluation confirms that the extracted controversy information is of high accuracy. A user study with 18 developers demonstrates the usefulness of our generated controversy summaries in helping developers avoid the controversial answers and choose more appropriate solutions to programming questions.

Automatic establishment of end-to-end multi-segment pseudo-wires by communication protocols is an effective way to improve the efficiency of path establishment. In this paper, the GMPLS protocol framework is adopted, and the layered nesting method is used to build the tunnel. Based on this, the mathematical model and analysis are carried out. An improved algorithm using RSVP-TE protocol is proposed to optimize the quality of service. The algorithm extends the protocol by adding related objects to carry pseudo-line information, and uses multi-layer nested forwarding adjacent way to establish the path on the boundary nodes of the network. The performance of RSVP-TE is analyzed experimentally, and the probability density function based on delay is introduced to calculate the mean, variance and packet loss rate of delay. The results show that compared with traditional LDP, this method reduces the delay and proves its superiority.

With the emergence of biometric technology in various applications, such as access control (e.g. mobile lock/unlock), financial transaction (e.g. Alibaba smile-to-pay) and time attendance, the development of biometric system attracts increasingly interest to the developers. Despite a sound biometric system gains the security assurance and great usability, it is a rather challenging task to develop an effective biometric system. For instance, many public available biometric APIs do not provide sufficient instructions / precise documentations on the usage of biometric APIs. Many developers are struggling in implementing these APIs in various tasks. Moreover, quick update on biometric-based algorithms (e.g. feature extraction and matching) may propagate to APIs, which leads to potential confusion to the system developers. Hence, we conduct an empirical study to the problems that the developers currently encountered while implementing the biometric APIs as well as the issues that need to be addressed when developing biometric systems using these APIs. We manually analyzed a total of 500 biometric API-related posts from various online media such as Stack Overflow and Neurotechnology. We reveal that 1) most of the problems encountered are related to the lack of precise documentation on the biometric APIs; 2) the incompatibility of biometric APIs cross multiple implementation environments.

GitHub is a large and popular open source project platform, which hosts various open source projects. Despite the prevalence of GitHub platform, not every project has gained high popularity. Identification of popular projects on GitHub can help developers choose proper projects to follow or contribute to, as well as provide guidance in building a popular project. In this paper, we propose an approach to predict the popularity of GitHub projects. We first conducted online surveys with GitHub users to determine the threshold (the number of stars of a project) of popular and unpopular projects. Next, we extract 35 features from both GitHub and Stack Overflow, which are divided into three dimensions: project, evolutionary, and project owner. A random forest classifier is built based on these features to identify popular GitHub projects. To evaluate the performance of our approach, we collect a large-scale dataset from GitHub which contains a total of 409,784 GitHub projects and 174,784 GitHub users. Our model achieves an average AUC of 0.76, which statistically significantly improves state-of-the-art by a substantial margin. We also study which features are of the most importance in distinguishing popular projects from unpopular ones. Experimental results show that number of branches, number of open issues, and number of contributors play the most important roles in identification of popular projects, and all of them have large effect size.

Branching is often used to help developers work in parallel during distributed software development. Previous studies have examined branch usage in practice. However, most studies perform branch analysis on industrial projects or only a small number of open source software (OSS) systems. There are no broad examinations of how branches are used across OSS communities. Due to the rapidly increasing popularity of collaboration in OSS projects, it is important to gain insights into the practice of branch usage in these communities. In this paper, we performed an empirical study on branch usage for 2,923 projects developed on GitHub. Our work mainly studies the way developers use branches and the effects of branching on the overall productivity of these projects. Our results show that: 1) Most projects use a few branches (

Programming screencasts have two important applications in software engineering context: study developer behaviors, information needs and disseminate software engineering knowledge. Although programming screencasts are easy to produce, they are not easy to analyze or index due to the image nature of the data. Existing techniques extract only content from screencasts, but ignore workflow actions by which developers accomplish programming tasks. This significantly limits the effective use of programming screencasts in downstream applications. In this paper, we are the first to present a novel technique for recognizing workflow actions in programming screencasts. Our technique exploits image differencing and Convolutional Neural Network (CNN) to analyze the correspondence and change of consecutive frames, based on which nine classes of frequent developer actions can be recognized from programming screencasts. Using programming screencasts from Youtube, we evaluate different configurations of our CNN model and the performance of our technique for developer action recognition across developers, working environments and programming languages. Using screencasts of developers' real work, we demonstrate the usefulness of our technique in a practical application for action-aware extraction of key-code frames in developers' work.

Software testing is an integral part of software development process. Unfortunately, for many projects, bugs are prevalent despite testing effort, and testing continues to cost significant amount of time and resources. This brings forward the issue of test case quality and prompts us to investigate what make good test cases. To answer this important question, we interview 21 and survey 261 practitioners, who come from many small to large companies and open source projects distributed in 27 countries, to create and validate 29 hypotheses that describe characteristics of good test cases and testing practices. These characteristics span multiple dimensions including test case contents, size and complexity, coverage, maintainability, and bug detection. We present highly rated characteristics and rationales why practitioners agree or disagree with them, which in turn highlight best practices and trade-offs that need to be considered in the creation of test cases. Our findings also highlight open problems and opportunities for software engineering researchers to improve practitioner activities and address their pain points.

Current state-of-the-art automatic software repair (ASR) techniques rely heavily on incomplete specifications, or test suites, to generate repairs. This, however, may cause ASR tools to generate repairs that are incorrect and hard to generalize. To assess patch correctness, researchers have been following two methods separately: (1) Automated annotation, wherein patches are automatically labeled by an independent test suite (ITS) - a patch passing the ITS is regarded as correct or generalizable, and incorrect otherwise, (2) Author annotation, wherein authors of ASR techniques manually annotate the correctness labels of patches generated by their and competing tools. While automated annotation cannot ascertain that a patch is actually correct, author annotation is prone to subjectivity. This concern has caused an on-going debate on the appropriate ways to assess the effectiveness of numerous ASR techniques proposed recently. In this work, we propose to assess reliability of author and automated annotations on patch correctness assessment. We do this by first constructing a gold set of correctness labels for 189 randomly selected patches generated by 8 state-of-the-art ASR techniques through a user study involving 35 professional developers as independent annotators. By measuring inter-rater agreement as a proxy for annotation quality - as commonly done in the literature - we demonstrate that our constructed gold set is on par with other high-quality gold sets. We then compare labels generated by author and automated annotations with this gold set to assess reliability of the patch assessment methodologies. We subsequently report several findings and highlight implications for future studies.

Coding proficiency is essential to software practitioners. Unfortunately, our understanding on coding proficiency often translates to vague stereotypes, e.g., "able to write good code". The lack of specificity hinders employers from measuring a software engineer's coding proficiency, and software engineers from improving their coding proficiency skills. This raises an important question: what skills matter to improve one's coding proficiency. To answer this question, we perform an empirical study by surveying 340 software practitioners from 33 countries across 5 continents. We first identify 38 coding proficiency skills grouped into nine categories by interviewing 15 developers from three companies. We then ask our survey respondents to rate the level of importance for these skills, and provide rationales of their ratings. Our study highlights a total of 21 important skills that receive an average rating of 4.0 and above (important and very important), along with rationales given by proponents and dissenters. We discuss implications of our findings to researchers, educators, and practitioners.

SANER 2019, the 26th IEEE International IEEE International Conference on Software Analysis, Evolution and Reengineering. SANER is the premier research conference on the theory and practice of recovering information from existing software and systems. It explores innovative methods of extracting the many kinds of information that can be recovered from software, software engineering documents, and systems artifacts, and examines innovative ways of using this information in system renovation and program understanding. SANER 2019 has a rich and diverse program on the latest innovations and visions in software analysis, evolution and reengineering. The research track received 151 submissions, of which 2 were desk-rejected and 1 was withdrawn by the authors during the review period. The remaining 148 papers each received at least three reviews. We adopted a double-blind reviewing process to the research track based on the success of double-blind reviewing adopted last year. Following an extensive online discussion, 45 papers were accepted (5 of them being conditionally accepted in a first stage), yielding an acceptance rate of 30.4%. No specific acceptance rate was communicated as a guideline to the reviewers. The PC were free to accept any paper deemed strong and novel enough and were encouraged to look for reasons to accept papers rather than to just point out flaws. The accepted papers will be published in the official conference proceedings. Up to top 10% of accepted papers would be awarded the IEEE TCSE Distinguished Paper Awards. The award papers will be announced at the conference. As in previous years, authors of selected papers from the research track will be invited to submit extended versions of their work to a special issue of the Springer international journal of Empirical Software Engineering (EMSE).

To share vulnerability information across separate databases, tools, and services, newly identified vulnerabilities are recurrently reported to Common Vulnerabilities and Exposures (CVE) database.Unfortunately, not all vulnerability reports will be accepted. Some of them might get rejected or be accepted with disputations.In this work, we refer to those rejected or disputed CVEs as invalid vulnerability reports. Invalid vulnerability reports not only cause unnecessary efforts to confirm the vulnerability but also impact the reputation of the software vendors. In this paper, we aim to understand the root causes of invalid vulnerability reports and build a prediction model to automatically identify them.To this end, we first leverage card sorting to categorize invalid vulnerability reports, from which six main reasons are observed for rejected and disputed CVEs, respectively.Then, we propose a text mining approach to predict the invalid vulnerability reports. Our experiments reveal that the proposed text mining approach can achieve an AUC score of 0.87 for predicting invalid vulnerabilities. We also discuss the implications of our study: our categorization can be used to guide new committer to avoid these traps; some root causes of invalid CVEs can be avoided by using automatic techniques or optimizing reviewing mechanism; invalid vulnerability reports data should not be neglected.

In smart mariculture, an automatic and accurate prediction of key water quality parameters is a significant and challenge issue. This paper focuses on the prediction of pH and water temperature parameters in key water quality parameters. Firstly, the water quality parameters are preprocessed by improved method. Then, the Pearson correlation coefficient method is used to find the correlation between the water quality parameters. Finally, the SRU (Simple Recurrent Unit) deep learning model is used to establish a prediction model for the key water quality parameters, so as to achieve accurate prediction. Meanwhile, we also evaluate the prediction effect of prediction model built by RNN (Recurrent Neural Network) deep learning network. The experimental results show that the proposed prediction method has higher prediction accuracy than the method based on RNN when the time complexity is similar. The proposed method takes 11.3ms to predict every data in average, and the prediction accuracy can reach 98.91%.

Software change-proneness prediction (whether or not class files in a project will be changed in the next release) can help software developers to focus on preventive actions to reduce maintenance costs, and managers to allocate resources more effectively. Prior studies found that change-proneness prediction works well if there is sufficient amount of training data to build a model. However, it is not feasible for projects with limited historical data especially for new projects. To address this issue, cross-project change-proneness prediction, which builds a prediction model by using data in another project (i.e., source project), and predicts the change-proneness in a target project, is proposed. Considering there are a large number of source projects, one challenge for cross-project change-proneness prediction is that given a target project, how to automatically select a source project which could show good prediction accuracy on it. In this paper, we propose a selective cross-project (SCP) model for change-proneness prediction. SCP automatically finds the source project which has the similar data distribution with the target project by measuring distribution similarity between source and target projects. We evaluate SCP by conducting an empirical study on 14 open source projects. We compare it with 2 most related change-proneness models, including RCP (Random Cross-Project prediction) proposed by Malhotra and Bansal, and CLAMI+ developed by Yan et al. Experiment results show that SCP improves RCP and CLAMI+ by 25.34% and 4.30% in terms of AUC respectively; and by 171.42% and 172.31% in terms of cost-effectiveness, respectively.

Ubuntu is an open source software platform that runs everywhere from the smartphone, the tablet and the PC to the server and the cloud. In Ubuntu, there are many self-contained or third-party software packages for different use, and a bug report in Ubuntu could affect one or more packages simultaneously. Identifying the common package bugs in Ubuntu can help both developers and users better understand the packages they are developing or using, and also provide further guidelines to developers of similar packages in the future. In this paper, we perform a large-scale empirical study of common package bugs on Ubuntu by leveraging topic modeling. By analyzing a total of 240,097 bug reports, we identify 3 general bugs that are common to all Ubuntu packages, i.e., Graphical User Interface (GUI), Maintenance, and Runtime bugs. Moreover, we categorize top-100 packages with most number of bug reports into 6 categories (i.e., graphics, internet, office, sound and video, system management, and kernel), and identify domain-specific bugs for each category.

Considering vehicle sideslip angle estimation difficulty under severe driving conditions with dynamic model based method due to vehicle nonlinear characteristic and parameter uncertainty, a novel kinematic model based method is proposed with the fusion of intelligent vehicle sensors. The state space models of the vehicle yaw angle and the roll angle are constructed based on the IMU and the lateral arrangement of the dual-GPS. In order to reduce the weight of the previously estimated value, an adaptive fading Kalman filtering algorithm is adopted to improve the filtering effect on the yaw and roll angle. A nonlinear adaptive observer is constructed to estimate the vehicle sideslip angle with the integration of the road line information from the camera, velocity from the GPS and acceleration/ angular velocity from the IMU. Furthermore, compared with the IMU, the information obtained from the GPS and the camera can't be utilized directly as large measurement delay. Thus, an observer-predictor is developed with multi-sensor fusion to handle the measurement delay problem. Finally, the proposed algorithm is validated through co-simulation under different maneuvers.