Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning

Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging. Although punishing RL agents for risky situations can help to learn safe policies, it may also lead to highly conservative behavior. In this paper, we propose a distributional RL framework in order to learn adaptive policies that can tune their level of conservativity at run-time based on the desired comfort and utility. Using a proactive safety verification approach, the proposed framework can guarantee that actions generated from RL are fail-safe according to the worst-case assumptions. Concurrently, the policy is encouraged to minimize safety interference and generate more comfortable behavior. We trained and evaluated the proposed approach and baseline policies using a high level simulator with a variety of randomized scenarios including several corner cases which rarely happen in reality but are very crucial. In light of our experiments, the behavior of policies learned using distributional RL can be adaptive at run-time and robust to the environment uncertainty. Quantitatively, the learned distributional RL agent drives in average 8 seconds faster than the normal DQN policy and requires 83\% less safety interference compared to the rule-based policy with slightly increasing the average crossing time. We also study sensitivity of the learned policy in environments with higher perception noise and show that our algorithm learns policies that can still drive reliable when the perception noise is two times higher than the training configuration for automated merging and crossing at occluded intersections

Impact of Human Resources Management Practices on Turnover, Productivity and Corporate Financial Performance

In developing countries, the human resource availability is quite easy but the most unfortunate part is its effective and efficient management. It is a well established fact that it is human beings behind the machines which can drive or drown the organizations. Human behavior and psychology is driven and motivated by varying degree of factors. The researchers across the globe have evolved and successfully practiced certain HRM techniques in order to achieve best performance and productivity from human capital. Unfortunately this area remained neglected and human resource could not be exploited to its full potential in Pakistan despite the fact that the country possesses  one of the best human capital in the world. This paper is an Endeavour to identify the best Human Resource Management practices applicable to Pakistani environments and analyze their positive effects on labor turnover, productivity and corporate financial performance. In order to achieve this objective, a survey questionnaire was designed and disseminated among respondents. A total of 200 questionnaires were distributed, out of which 145 completed questionnaires were received. The authors analyzed the data by using statpro software. the major conclusions and findings were; Need for articulation of vision, mission and values for organization, lack of performance management system, lack of benefit and compensation program, issue of corporate loyalty, poor workforce alignment, absence of HR development and training programs, lack of Human Resource Information System(HRIS),and non adoption of TQM. Keywords: HRM, Productivity, Pakistan, Turnover

Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model

Peer reviewe

Automation of the UNICARagil Vehicles

The German research project UNICARagil is a collaboration between eight universities and six industrial partners funded by the Federal Ministry of Education and Research. It aims to develop innovative modular architectures and methods for new agile, automated vehicle concepts. This paper summarizes the automation approach of the driverless vehicle concept and its modular realization within the four demonstration vehicles to be built by the consortium. On-board each vehicle, this comprises sensor modules for environment perception and modelling, motion planning for normal driving and safe halts, as well as the respective control algorithms and base functionalities like precise localization. A control room and cloud functionalities provide off-board support to the vehicles, which are additionally addressed in this paper

Multiclass Brain Tumor Classification from MRI Images using Pre-Trained CNN Model

A brain tumor is an accumulation of malignant cells that results from unrestrained cell division. Tumors can result in crucial effects if they are not promptly and accurately recognized. Misdiagnosis can result in ineffective therapy, which decreases the patient's survival rate. The standard procedure for determining the presence of brain tumors and the type of tumors is magnetic resonance imaging (MRI). But as technology advances, it gets harder to comprehend huge amounts of data generated in an acceptable time. However, building a deep learning model from the start requires collecting enormous amounts of labeled data, which is a costly, time-consuming operation. A method to solve these issues is transfer learning of a deep learning model that has already been trained on the ImageNet dataset. In this research, the classification of brain tumors using several pre-trained deep learning models, i.e., different variations of ResNet, VGG, and DenseNet models, are being trained on a brain tumor dataset and compared. According to experiments, the ResNet50 model with a fine-tuned and transfer learning approach has achieved the highest training accuracy of 99%, validation accuracy of 96%, and test accuracy of 80%.

Back to the Future: Solving Hidden Parameter MDPs with Hindsight

AlgorithmicsInteractive Intelligenc