Adaptive Multi-Class Audio Classification in Noisy In-Vehicle Environment

With ever-increasing number of car-mounted electric devices and their complexity, audio classification is increasingly important for the automotive industry as a fundamental tool for human-device interactions. Existing approaches for audio classification, however, fall short as the unique and dynamic audio characteristics of in-vehicle environments are not appropriately taken into account. In this paper, we develop an audio classification system that classifies an audio stream into music, speech, speech+music, and noise, adaptably depending on driving environments including highway, local road, crowded city, and stopped vehicle. More than 420 minutes of audio data including various genres of music, speech, speech+music, and noise are collected from diverse driving environments. The results demonstrate that the proposed approach improves the average classification accuracy up to 166%, and 64% for speech, and speech+music, respectively, compared with a non-adaptive approach in our experimental settings

SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone

The number of pedestrian accidents continues to keep climbing. Distraction from smartphone is one of the biggest causes for pedestrian fatalities. In this paper, we develop SaferCross, a mobile system based on the embedded sensors of smartphone to improve pedestrian safety by preventing distraction from smartphone. SaferCross adopts a holistic approach by identifying and developing essential system components that are missing in existing systems and integrating the system components into a "fully-functioning" mobile system for pedestrian safety. Specifically, we create algorithms for improving the accuracy and energy efficiency of pedestrian positioning, effectiveness of phone activity detection, and real-time risk assessment. We demonstrate that SaferCross, through systematic integration of the developed algorithms, performs situation awareness effectively and provides a timely warning to the pedestrian based on the information obtained from smartphone sensors and Direct Wi-Fi-based peer-to-peer communication with approaching cars. Extensive experiments are conducted in a department parking lot for both component-level and integrated testing. The results demonstrate that the energy efficiency and positioning accuracy of SaferCross are improved by 52% and 72% on average compared with existing solutions with missing support for positioning accuracy and energy efficiency, and the phone-viewing event detection accuracy is over 90%. The integrated test results show that SaferCross alerts the pedestrian timely with an average error of 1.6sec in comparison with the ground truth data, which can be easily compensated by configuring the system to fire an alert message a couple of seconds early.Comment: Published in IEEE Access, 202

Detection of Sensor Attack and Resilient State Estimation for Uniformly Observable Nonlinear Systems having Redundant Sensors

This paper presents a detection algorithm for sensor attacks and a resilient state estimation scheme for a class of uniformly observable nonlinear systems. An adversary is supposed to corrupt a subset of sensors with the possibly unbounded signals, while the system has sensor redundancy. We design an individual high-gain observer for each measurement output so that only the observable portion of the system state is obtained. Then, a nonlinear error correcting problem is solved by collecting all the information from those partial observers and exploiting redundancy. A computationally efficient, on-line monitoring scheme is presented for attack detection. Based on the attack detection scheme, an algorithm for resilient state estimation is provided. The simulation results demonstrate the effectiveness of the proposed algorithm

On Redundant Observability: From Security Index to Attack Detection and Resilient State Estimation

The security of control systems under sensor attacks is investigated. Redundant observability is introduced, explaining existing security notions including the security index, attack detectability, and observability under attacks. Equivalent conditions between redundant observability and existing notions are presented. Based on a bank of partial observers utilizing Kalman decomposition and a decoder exploiting redundancy, an estimator design algorithm is proposed enhancing the resilience of control systems. This scheme substantially improves computational efficiency utilizing far less memory

Dynamic Vehicular Route Guidance Using Traffic Prediction Information

We propose a dynamic vehicular routing algorithm with traffic prediction for improved routing performance. The primary idea of our algorithm is to use real-time as well as predictive traffic information provided by a central routing controller. In order to evaluate the performance, we develop a microtraffic simulator that provides road networks created from real maps, routing algorithms, and vehicles that travel from origins to destinations depending on traffic conditions. The performance is evaluated by newly defined metric that reveals travel time distributions more accurately than a commonly used metric of mean travel time. Our simulation results show that our dynamic routing algorithm with prediction outperforms both Static and Dynamic without prediction routing algorithms under various traffic conditions and road configurations. We also include traffic scenarios where not all vehicles comply with our dynamic routing with prediction strategy, and the results suggest that more than half the benefit of the new routing algorithm is realized even when only 30% of the vehicles comply

Resilient State Estimation for Control Systems Using Multiple Observers and Median Operation

This paper addresses the problem of state estimation for linear dynamic systems that is resilient against malicious attacks on sensors. By “resiliency” we mean the capability of correctly estimating the state despite external attacks. We propose a state estimation with a bank of observers combined through median operations and show that the proposed method is resilient in the sense that estimated states asymptotically converge to the true state despite attacks on sensors. In addition, the effect of sensor noise and process disturbance is also considered. For bounded sensor noise and process disturbance, the proposed method eliminates the effect of attack and achieves state estimation error within a bound proportional to those of sensor noise and disturbance. While existing methods are computationally heavy because online solution of nonconvex optimization is needed, the proposed approach is computationally efficient by using median operation in the place of the optimization. It should be pointed out that the proposed method requires the system states being observable with every sensor, which is not a necessary condition for the existing methods. From resilient system design point of view, however, this fact may not be critical because sensors can be chosen for resiliency in the design stage. The gained computational efficiency helps real-time implementation in practice

Continuous productivity improvement using ioe data for fault monitoring: An automotive parts production line case study

This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1