Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation

Risk Profiles in Individual Software Development and Packaged Software Implementation Projects: A Delphi Study at a German-Based Financial Services Company

The aim of this paper is to compare risk profiles of individual software development (ISD) and packaged software implementation (PSI) projects. While researchers have investigated risks in either PSI projects or ISD projects, an integrated perspective on how the risk profiles of these two types of information system (IS) projects differ is missing. To explore these differences, this work conducted a Delphi study at a German-based financial services company. The results suggest that: First, ISD projects seem to be more heterogeneous and face a larger variety of risks than the more straightforward PSI projects. Second, ISD projects seem to be particularly prone to risks related to sponsorship, requirements, and project organization. Third, PSI projects tend to be predominantly subject to risks related to technology, project planning, and project completion. Finally, in contrast to available lists of risks in IS projects and irrespective of the project type, the paper found a surprisingly high prominence of technology and testing-related risks

Towards Understanding the Relative Importance of Risk Factors in IS Projects: A Quantitative Perspective

Commonly, project managers and researchers agree that identifying risks is the most crucial step in project risk management. Hence, extant research provides various rankings of risk factors. In this paper, we rank the importance of risk factors based on an archive of project risk reports provided by project managers of a large software development company. In contrast to previous research that ranks people and processes as most important risk domains, our analysis emphasizes technologyrelated risk factors. We argue that this conflict might result from two dimensions determining the perceived importance of risk factors: Controllability and micro-politics. A project manager will rank risks higher when he has only limited control on mitigating risks. Risks beyond control will be neglected. However, in a corporate context, micro-political mechanisms change the importance towards these risks. They will exploit risk management to escalate uncontrollable threats to project success and cover risk factors that stem from shortcomings of their own or of colleagues. Thus, micropolitical mechanisms reveal the most important risks from a corporate perspective. Detached from the corporate context, project managers emphasize risks threatening efficient project management. We contribute to IS research by proposing alternative explanations for the ranking discrepancies

When to manage risks in IS projects: An exploratory analysis of longitudinal risk reports

Research attributes the mixed performance of IS projects to a poorunderstanding of risks and thus limited capabilities to manage suchrisks. In line with others, we argue that the poor understanding ofrisks is partly due to the fact, that current research almostexclusively concentrates on which risks are important in ISprojects. In contrast to this static view, we focus on the temporalaspect of project risks, i.e., we explore when risks become more orless important during a project. In doing so, we analyze an archiveof risk reports of completed enterprise software projects. Projectmanagers regularly issued the risk reports to communicate thestatus of the particular project. Our findings are as follows: First,risk exposure and thus the perceived importance of risk types doesvary over project phases. Second, the volatility of risk exposurevaries over risk types and project phases. Third, risks of variousorigin exhibit synchronous changes in risk exposure over time.From a research perspective, these findings substantiate the needfor a temporal perspective on IS project risks. Thus, we suggestaugmenting the predominant static view on project risks to helpproject managers in focusing their scarce resources. From apractical perspective, we highlight the benefits of regularlyperforming risk management throughout projects and constantlyanalyzing the project portfolio. In sum, we provide a first time,descriptive and exploratory view on variations in project riskassessments over time

Determinants of vendor profitability in two contractual regimes: an empirical analysis of enterprise resource planning projects

In this paper, we investigate the effects of four determinants of vendor profitability in enterprise resource planning (ERP) outsourcing projects under two contractual regimes: fixed price (FP) contracts and time and material (TM) contracts. We hypothesize that effect sizes are larger under FP contracts than under TM contracts. From a transaction cost economics perspective, we hypothesize that project uncertainty and project size are negatively associated with vendor profitability. From a knowledge-based view of the firm perspective, we hypothesize that industry knowledge and client knowledge are positively associated with vendor profitability. We tested these hypotheses on a comprehensive archival data set comprising 33,908 projects from a major vendor in the ERP software market. Our results confirm and extend previous research. Our results support the existence of two contractual regimes: effect sizes on vendor profitability are indeed much larger in FP contracts than in TM contracts. Also in line with prior research, our results suggest negative effects of project uncertainty and project size in terms of project budget on vendor profitability and positive effects of industry knowledge on vendor profitability. Contrary to prior knowledge, we find that project size in terms of project duration is significantly positively associated with vendor profitability in FP contracts. Also contrary to what is known, we find a significant negative effect of client knowledge on vendor profitability in both contractual regimes

CALCULATING THE CONDITIONAL VALUE AT RISK IN IS PROJECTS: TOWARDS A SINGLE MEASURE OF PROJECT RISK

Risk management in IT projects still is more an art than a science. Reliable figures about the risks of a project portfolio still depend on intuition and experience of project managers. A central challenge is to aggregate the risks of a project into a single risk measure that makes it easy for the senior management to compare projects and see which projects need their attention. We first analyze different approaches to aggregate risks and compare them in terms of theoretical foundation and practical usability. In particular we explore the applicability of the well-known financial risk figure Conditional Value-at-Risk (CVaR). Using data from 110 IT projects we demonstrate that the CVaR offers a well-defined risk measure that provides clear information for senior management decision-making. Since the CVaR is flexible concerning its confidence level it can be changed to fit the management’s risk aversion. Finally, we derive suggestions for risk management to make the calculated CVaR even more reliable. In sum, we show that well-defined risk measures can be transferred to the domain of project risk management if companies establish central risk reporting

CALCULATING THE CONDITIONAL VALUE AT RISK IN IS PROJECTS: TOWARDS A SINGLE MEASURE OF PROJECT RISK

Risk management in IT projects still is more an art than a science. Reliable figures about the risks of a project portfolio still depend on intuition and experience of project managers. A central challenge is to aggregate the risks of a project into a single risk measure that makes it easy for the senior management to compare projects and see which projects need their attention. We first analyze different approaches to aggregate risks and compare them in terms of theoretical foundation and practical usability. In particular we explore the applicability of the well-known financial risk figure Conditional Value-at-Risk (CVaR). Using data from 110 IT projects we demonstrate that the CVaR offers a well-defined risk measure that provides clear information for senior management decision-making. Since the CVaR is flexible concerning its confidence level it can be changed to fit the management’s risk aversion. Finally, we derive suggestions for risk management to make the calculated CVaR even more reliable. In sum, we show that well-defined risk measures can be transferred to the domain of project risk management if companies establish central risk reporting