On Leader Following and Classification

International audienceService and assistance robots that must move in human environment must address the difficult issue of navigating in dynamic environments. As it has been shown in previous works, in such situations the robots can take advantage of the motion of persons by following them, managing to move together with humans in difficult situations. In those circumstances, the problem to be solved is how to choose a human leader to be followed. This work proposes an innovative method for leader selection, based on human experience. A learning framework is developed, where data is acquired, labeled and then used to train an AdaBoost classification algorithm, to determine if a candidate is a bad or a good leader, and also to study the contribution of features to the classification process

Mobile Robot Navigation for Person Following in Indoor Environments

Service robotics is a rapidly growing area of interest in robotics research. Service robots inhabit human-populated environments and carry out specific tasks. The goal of this dissertation is to develop a service robot capable of following a human leader around populated indoor environments. A classification system for person followers is proposed such that it clearly defines the expected interaction between the leader and the robotic follower. In populated environments, the robot needs to be able to detect and identify its leader and track the leader through occlusions, a common characteristic of populated spaces. An appearance-based person descriptor, which augments the Kinect skeletal tracker, is developed and its performance in detecting and overcoming short and long-term leader occlusions is demonstrated. While following its leader, the robot has to ensure that it does not collide with stationary and moving obstacles, including other humans, in the environment. This requirement necessitates the use of a systematic navigation algorithm. A modified version of navigation function path planning, called the predictive fields path planner, is developed. This path planner models the motion of obstacles, uses a simplified representation of practical workspaces, and generates bounded, stable control inputs which guide the robot to its desired position without collisions with obstacles. The predictive fields path planner is experimentally verified on a non-person follower system and then integrated into the robot navigation module of the person follower system. To navigate the robot, it is necessary to localize it within its environment. A mapping approach based on depth data from the Kinect RGB-D sensor is used in generating a local map of the environment. The map is generated by combining inter-frame rotation and translation estimates based on scan generation and dead reckoning respectively. Thus, a complete mobile robot navigation system for person following in indoor environments is presented

Analyzing Crash Potential in Mixed Traffic with Autonomous and Human-Driven Vehicles

Reducing crash counts on saturated road networks is one of the most significant benefits behind the introduction of Autonomous Vehicle (AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but less attention has been paid to the car-following scenarios between AVs and human drivers. A mismatch in the braking and accelerating decisions in this car-following scenario can lead to rear-end near-crashes and therefore needs to be studied. This thesis aims to investigate the driving behavior of human-drivers that follow a designated AV leader in a car-following situation and compare the results with a scenario when the leader is a human-like driver. In this study, speed trajectory data was collected from 48 participants using a driving simulator. To estimate the near-crash risk between the participants and the leading vehicle, critical thresholds of six Surrogate Safety Measures (SSMs): Time to Collision (TTC), Inverse Time to Collision (ITTC), Modified Time to Collision (MTTC), Deceleration Rate to Avoid Crash (DRAC), critical jerk and Warning Index (WI), were used. The potential near-crash events and the safe driving events were classified using a random forest algorithm after performing oversampling and undersampling techniques. The results from the two-sample t-tests indicated a significant difference between the overall deceleration rates, braking speeds, and acceleration rates of the participants and the designated AV leader. However, no such difference was found between the participants and the human-like leader while braking and accelerating at stop-controlled intersections. Out of six SSMs, MTTC detected near-crash events 10 seconds before their actual occurrence at a range of 11.93 m with 83% accuracy. The surrogate measures identified a higher number of near-crash (high risk) events when the participants followed the designated AV and made braking maneuvers at the stop-controlled intersections. Based on the number of near-crash (high risk) events, the designated AV's C3.25 speed profile (with the maximum deceleration rate of 3.25 m/s2 ) posed the highest crash risk to the participants in the following vehicle. For potential near-crash events classification, a random forest classifier based on undersampled data achieved the highest average accuracy rate of 92.2%. The deceleration rates of the designated AV had the highest impact on the near-crashes between the AV and the participants. However, shorter clearances during the braking maneuvers at intersections significantly affected the near-crashes between the human-like leader and the participants in the following vehicle

Software Ecosystem Governance and Participation - a Case Study at Axis Communications AB

Traditionally software development was performed by one single company. However, the traditional roles are being redefined and software ecosystems are taking software development outside the borders of the software company. Companies are starting to collaborate by using digital platforms and are creating new complex networks of organizations, so called software ecosystems. Software ecosystems are a relatively new area of research and a collective theoretical foundation is starting to emerge. In a software ecosystem, surrounding a platform, the governance activities and decisions of the platform leader influence everyone involved. Therefore it is interesting to understanding how the factors behind participation are affected by the governance performed by a platform leader. The purpose of this master’s thesis is to explore how governance performed by a platform leader affects developer participation in a software ecosystem. The following three research questions were answered: 1.Why do third party developers to join and participate in Axis’ software ecosystem? 2.What makes third party developers hesitant towards joining Axis' software ecosystem? 3.What governance activities are performed by Axis as a platform leader? Due to the exploratory nature of the master’s thesis a case study approach was chosen. The process of work was conducted in accordance with the case study process presented by Runeson and Höst (2009). Qualitative data was collected through interviews at Axis and with external developers connected to the software ecosystem. Additional information was collected through two surveys and written documentation. Through classifying the software ecosystem according to existing classification models and by analyzing the collected data the research questions were answered and created an understanding for how the governance activities performed by the platform leader affect participation in this software ecosystem The findings of this master’s thesis indicate that both non performed and performed governance activities by the platform leader has an effect on participation in a software ecosystem, and that the contextual factors set the stage for which governance activities that will be most influential. Furthermore, unsystematically performed governance activities were found to increase the need of a personal relationship and good communication between the platform leader and third party developers in order for the latter to participate. Additionally, the creation and stabilization of application programming interfaces (API:s) were found to be important for developer participation in a software ecosystem where the platform leader offer several product lines. Finally, by not selling directly to end customers the platform leader’s power is diffused over many vendors. This reduces the effects of governance activities and prevents enactment of others; hence reduce the platform leader’s influence on participation

Leadership Typology and Employee Engagement

Purpose: The paper is a contribution to research on managers / leaders performance influencing employee engagement. The manager has a major impact on the level of employee commit-ment. There is no unified, complete, empirically verified model of managerial activity leading to employee engagement in the subject literature. The available studies indicate numerous connections between the different aspects of the leader’s performance and employee engagement. Based on the findings of the literature review, the authors defined the concept of employee engagement and the importance of a lead-er’s role in engagement building. Significant managers’ actions affecting employee engagement have been identified. Due to the complex nature of the leader’s activity, the following classifica-tion identifying four model profiles has been proposed: a classical leader, a change leader, a discreet leader and a holistic leader. Each of the profiles has been characterised by the tasks performed.Design/methodology/approach: The results of the quantitative research on a representative sample of professionally active Poles conducted by the Institute of Human Capital at Warsaw School of Economics in the autumn of 2016 identified the prevalence of employees opinions about model behaviours of managers. It was measured which patterns are the most common. Both fully and partially saturated behavioural patterns have been tested (how many managers meet the patterns in full, in 75% and in half), as well as pure and mixed variants of models.Findings: On the basis of the analyses it was determined that the most beneficial, considering employee engagement, is the model combining the behavioural characteristics of all three patterns. Further research on leadership should take into account the com-plexity of a leader’s role, particularly in the context of manage-ment through engagement.Research and practical limitations / implications: The anal-yses reveal that managers have a major impact on employee engagement. In order to maximise efficiency, managers should apply comprehensive skills appropriate in the given stage of team management.Originality/value: The paper is a contribution to the discussion on the nature of engagement, leadership and the relationship between these two constructs. Based on the analysis of the leadership paradigms, the authors’ original classification of key behavioural patterns of engaging leaders has been proposed.Paper type: Research paper