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

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Analysis of adaptive algorithms for an integrated communication network

Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes

Delay Constrained Throughput Analysis of a Correlated MIMO Wireless Channel

The maximum traffic arrival rate at the network for a given delay guarantee (delay constrained throughput) has been well studied for wired channels. However, few results are available for wireless channels, especially when multiple antennas are employed at the transmitter and receiver. In this work, we analyze the network delay constrained throughput of a multiple input multiple output (MIMO) wireless channel with time-varying spatial correlation. The MIMO channel is modeled via its virtual representation, where the individual spatial paths between the antenna pairs are Gilbert-Elliot channels. The whole system is then described by a K-State Markov chain, where K depends upon the degree of freedom (DOF) of the channel. We prove that the DOF based modeling is indeed accurate. Furthermore, we study the impact of the delay requirements at the network layer, violation probability and the number of antennas on the throughput under different fading speeds and signal strength.Comment: Submitted to ICCCN 2011, 8 pages, 5 figure

Development and validation of the Spanish hazard perception test

Objective: The aim of the current study is to develop and obtain validity evidence for a Hazard Perception test suitable for the Spanish driving population. To obtain validity evidence to support the use of the test, the effect of hazardous and quasi-hazardous situations on the participants’ Hazard Prediction is analysed and the pattern of results of drivers of different driving experience: learner, novice and expert drivers and re-offender vs. non-offender drivers, is compared. Potentially hazardous situations are those that develop without involving any real hazard (i.e., the driver didn’t actually have to decelerate or make any evasive manoeuvre to avoid a potential collision). The current study analysed multiple offender drivers attending compulsory re-education programmes as a result of reaching the maximum number of penalty points on their driving licence, due to repeated violations of traffic laws. Method: A new video-based hazard perception test was developed, using a total of 20 hazardous situation videos plus 8 quasi-hazardous situation videos. They were selected from 167 recordings of natural hazards in real Spanish driving settings