Abstract

International audience; The study of round-trip time (RTT) measurements on the Internet is of particular importance for improving real-time applications, enforcing QoS with traffic engineering, or detecting unexpected network conditions. On large timescales, from 1 hour to several days, RTT measurements exhibit characteristic patterns due to inter and intra-AS routing changes and traffic engineering, in addition to link congestion. We propose the use of a nonparametric Bayesian model, the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), to characterize RTT timeseries. The parameters of the HMM, including the number of states, as well as the values of hidden states are estimated from delay observations by Gibbs sampling. No assumptions are made on the number of states, and a nonparametric mixture model is used to represent a wide range of delay distribution in each state for more flexibility. We validate the model through three applications: on RIPE Atlas measurements we show that 80% of the states learned on RTTs match only one AS path; on a labelled delay changepoint dataset we show that the model is competitive with state-of-the-art changepoint detection methods in terms of precision and recall; and we show that the predictive ability of the model allows us to reduce the monitoring cost by 90% in routing overlays using Markov decision processes.

Abstract

International audience; Semi-autonomous driving assistance systems have a high potential to improve the safety and efficiency of the battery electric vehicles that are enduring limited cruising range. This paper presents an ecologically advanced driver assistance system to extend the functionality of the adaptive cruise control system. A real-time stochastic non-linear model predictive controller with probabilistic constraints is presented to compute on-line the safe and energy-efficient cruising velocity profile. The individual chance-constraint is reformulated into a convex second-order cone constraint which is robust for a general class of probability distributions. Finally, the performance of proposed approach in terms of states regulation, constraints fulfilment, and energy efficiency is evaluated on a battery electric vehicle.

Abstract

Long haulage road transport consumes considerable amounts of energy in today's world. Predictive control strategies based on digital maps can significantly lower the portion being wasted in traditional cruise control operated highway driving. Such control strategies rely on high quality stored road grade information. This paper describes a newly developed method to estimate the road grade using sensors commonly found on standard heavy duty vehicles. The method utilizes a piecewise linear road model derived from highway design methodologies. The estimation method has been implemented and evaluated experimentally, and is shown to give better results compared to an existing method. © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20120213

Abstract

The evaluation of driver's steering comfort, which is mainly concerned with the haptic driver–vehicle interaction, is important for the optimization of advanced driver assistance systems. The current approaches to investigating steering comfort are mainly based on the driver's subjective evaluation, which is time-consuming, expensive, and easily influenced by individual variations. This paper makes some tentative investigation of objective evaluation, which is based on the electromyogram (EMG) and movement trajectory of the driver's upper limbs during steering maneuvers. First, a steering experiment with 21 subjects is conducted, and EMG and movement trajectories of the driver's upper limbs are measured, together with their subjective evaluation of steering comfort. Second, five evaluation indices including EMG and movement information are defined based on the measurements from the first step. Correlation analyses are conducted between each evaluation index and steering comfort rating (SCR), and the results show that all of the indices have significant correlations with SCR. Then, an artificial neural network model is devised based on the aforementioned indices and its predicting performance of SCR is demonstrated as acceptable. The results reveal that it may be feasible to establish an objective evaluation approach for vehicle steering comfort. Document type: Article