A Deep Learning Based Model for Driving Risk Assessment

In this paper a novel multilayer model is proposed for assessing driving risk. Studying aggressive behavior via massive driving data is essential for protecting road traffic safety and reducing losses of human life and property in smart city context. In particular, identifying aggressive behavior and driving risk are multi-factors combined evaluation process, which must be processed with time and environment. For instance, improper time and environment may facilitate abnormal driving behavior. The proposed Dynamic Multilayer Model consists of identifying instant aggressive driving behavior that can be visited within specific time windows and calculating individual driving risk via Deep Neural Networks based classification algorithms. Validation results show that the proposed methods are particularly effective for identifying driving aggressiveness and risk level via real dataset of 2129 drivers’ driving behavior

Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving. A deep predictive model is trained to anticipate imminent accidents from traditional video streams. In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations. In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about the uncertainty inherent to the task. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.Comment: 8 pages, 4 figure

Failure is an option:an innovative engineering curriculum

PurposeAdvancements and innovation in engineering design are based on learning from previous failures but students are encouraged to ‘succeed’ first time and hence can avoid learning from failure in practice. The purpose of the study was to design and evaluate a curriculum to help engineering design students to learn from failure.Design/Methodology/ApproachA new curriculum design provided a case study for evaluating the effects of incorporating learning from failure within a civil engineering course. An analysis of the changes in course output was undertaken in relation to graduate destination data covering 2006 to 2016 and student satisfaction from 2012 to 2017 and a number of challenges and solutions for curriculum designers were identified.FindingsThe design and delivery of an innovative curriculum, within typical constraints, can provide opportunities for students to develop resilience to failure as an integral part of their learning in order to think creatively and develop novel engineering solutions. The key issues identified were: the selection of appropriate teaching methods, creating an environment for exploratory learning, group and team assessments with competitive elements where practicable, and providing students with many different pedagogical approaches to produce a quality learning experience.OriginalityThis case study demonstrates how to design and implement an innovative curriculum that can produce positive benefits of learning from failure. This model can be applied to other disciplines such as building surveying and construction management. This approach underpins the development of skills necessary in the educational experience to develop as a professional building pathologist