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

Door-to-Door Mobility Integrators as Keystone Organizations of Smart Ecosystems: Resources and Value Co-Creation – A Literature Review

Cities around the world face major mobility-related challenges, such as traffic congestion and air pollution. One primary cause of these challenges is the decision of citizens to use their private car instead of alternative mobility services such as public transport, car-sharing and bike-sharing. Technological progress offers new possibilities to address these challenges by making alternative mobility services easier and more convenient to use. This paper focuses on door-to-door (D2D) mobility integrators, which aim to offer citizens seamless D2D transport by packaging alternative mobility services. To better understand the practical barriers D2D mobility integrators face, this interdisciplinary literature review provides a holistic picture of their operand and operant resources, revealing significant gaps in our understanding of their capability to attract actors to their ecosystem and to manage value co-creation. Based on these gaps, we identify a potential avenue of future research