Automated Repair of Feature Interaction Failures in Automated Driving Systems

In the past years, several automated repair strategies have been proposed to fix bugs in individual software programs without any human intervention. There has been, however, little work on how automated repair techniques can resolve failures that arise at the system-level and are caused by undesired interactions among different system components or functions. Feature interaction failures are common in complex systems such as autonomous cars that are typically built as a composition of independent features (i.e., units of functionality). In this paper, we propose a repair technique to automatically resolve undesired feature interaction failures in automated driving systems (ADS) that lead to the violation of system safety requirements. Our repair strategy achieves its goal by (1) localizing faults spanning several lines of code, (2) simultaneously resolving multiple interaction failures caused by independent faults, (3) scaling repair strategies from the unit-level to the system-level, and (4) resolving failures based on their order of severity. We have evaluated our approach using two industrial ADS containing four features. Our results show that our repair strategy resolves the undesired interaction failures in these two systems in less than 16h and outperforms existing automated repair techniques

Effective Testing Of Advanced Driver Assistance Systems Using Evolutionary Algorithms And Machine Learning

Improving road safety is a major concern for most car manufacturers. In recent years, the development of Advanced Driver Assistance Systems (ADAS) has subsequently seen a tremendous boost. The development of such systems requires complex testing to ensure vehicle’s safety and reliability. Performing road tests tends to be dangerous, time-consuming, and costly. Hence, a large part of testing for ADAS has to be carried out using physics-based simulation platforms, which are able to emulate a wide range of virtual traffic scenarios and road environments. The main difficulties with simulation-based testing of ADAS are: (1) the test input space is large and multidimensional, (2) simulation platforms provide no guidance to engineers as to which scenarios should be selected for testing, and hence, simulation is limited to a small number of scenarios hand-picked by engineers, and (3) test executions are computationally expensive because they often involve executing high-fidelity mathematical models capturing continuous dynamic behaviors of vehicles and their environment. The complexity of testing ADAS is further exacerbated when many ADAS are employed together in a self-driving system. In particular, when self-driving systems include many ADAS (i.e., features), they tend to interact and impact one another’s behavior in an unknown way and may lead to conflicting situations. The main challenge here is to detect and manage feature interactions, in particular, those that violate system safety requirements, hence leading to critical failures. In practice, once feature interaction failures are detected, engineers need to devise resolution strategies to resolve potential conflicts between features. Developing resolution strategies is a complex task and despite the extensive domain expertise, these resolution strategies can be erroneous and are too complex to be manually repaired. In this dissertation, in addition to testing individual ADAS, we focus on testing self-driving systems that include several ADAS. In this dissertation, we propose a set of approaches based on meta-heuristic search and machine learning techniques to automate ADAS testing and to repair feature interaction failures in self-driving systems. The work presented in this dissertation is motivated by ADAS testing needs at IEE, a world-leading part supplier to the automotive industry. In this dissertation, we focus on the problem of design time testing of ADAS in a simulated environment, relying on Simulink models. The main research contributions in this dissertation are: - A testing approach for ADAS that combines multi-objective search with surrogate models to guide testing towards the most critical behaviors of ADAS, and to explore a larger part of the input search space with less computational resources. - An automated testing algorithm that builds on learnable evolution models and uses classification decision trees to guide the generation of new test scenarios within complex and multidimensional input spaces and help engineers interpret test results. - An automated technique that detects feature interaction failures in the context of self-driving systems based on analyzing executable function models typically developed to specify system behaviors at early development stages. - An automated technique that uses a new many-objective search algorithm to localize and repair errors in the feature interaction resolution rules for self-driving systems

A comparative study of using spindle motor power and eddy current for the detection of tool conditions in milling processes

This paper investigates the use of the power of the driving motor of a CNC spindle in comparison to two perpendicular eddy current sensors for the detection of tool wear in milling processes. Monitoring the power through the current profile is a low cost system which has been utilised in this study as an attempt to detect the fluctuation in the motor load as a result of the conditions of the cutting tool. Eddy current sensors are dedicated sensors that are installed on the spindle to measure the vibration of the rotating spindle in two axes. Experimental work has been conducted using fresh and worn tools to investigate the effect of tool conditions on the two sensory systems. Time domain features are utilised to compare between the two sensors in relation to this application. The results indicate that Eddy current sensors are found to be more successful and sensitive, in general, than the power of the motor in detecting the changes of the cutting tools during the machining operation. However, the kurtosis value of the power of the spindle has been found to be successful in predicting the tool conditions with high sensitivity

Automated Misconfiguration Repair of Configurable Cyber-Physical Systems with Search: an Industrial Case Study on Elevator Dispatching Algorithms

Real-world Cyber-Physical Systems (CPSs) are usually configurable. Through parameters, it is possible to configure, select or unselect different system functionalities. While this provides high flexibility, it also becomes a source for failures due to misconfigurations. The large number of parameters these systems have and the long test execution time in this context due to the use of simulation-based testing make the manual repair process a cumbersome activity. Subsequently, in this context, automated repairing methods are paramount. In this paper, we propose an approach to automatically repair CPSs' misconfigurations. Our approach is evaluated with an industrial CPS case study from the elevation domain. Experiments with a real building and data obtained from operation suggests that our approach outperforms a baseline algorithm as well as the state of the practice (i.e., manual repair carried out by domain experts).Comment: To be published in the 45th International Conference on Software Engineering, SEIP trac

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Exploring the Efficacy of Social Trust Repair in Human-Automation Interactions

ABSTRACT Trust is a critical component to both human-automation and human-human interactions. Interface manipulations, such as visual anthropomorphism and machine politeness, have been used to affect trust in automation. However, these design strategies have been primarily used to facilitate initial trust formation but have not been examined means to actively repair trust that has been violated by a system failure. Previous research has shown that trust in another party can be effectively repaired after a violation using various strategies, but there is little evidence substantiating such strategies in human-automation context. The current study examined the effectiveness of trust repair strategies, derived from a human-human or human-organizational context, in human-automation interaction. During a taxi dispatching task, participants interacted with imperfect automation that either denied or apologized for committing competence- or integrity-based failures. Participants performed two experimental blocks (one for each failure type), and, after each block, reported subjective trust in the automation. Consistent with interpersonal literature, our analysis revealed that automation apologies more successfully repaired trust following competence-based failures than integrity-based failures. However, user trust in automation was not significantly different when the automation denied committing competence- or integrity-based failures. These findings provide important insight into the unique ways in which humans interact with machines