Research Video: Safety In-Vehicle Services

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Are we vulnerable to petty crimes in autonomous shuttles? Check the demonstration of the in-vehicle services providing advanced security and trust developed by H2020 AVENUE partner CERTH-ITI here:

New Scientific Article: Audio-Based Event Detection at Different SNR Settings Using Two-Dimensional Spectrogram Magnitude Representations

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Abstract: Audio-based event detection poses a number of different challenges that are not encountered in other fields, such as image detection. Challenges such as ambient noise, low Signal-to-Noise Ratio (SNR) and microphone distance are not yet fully understood. If the multimodal approaches are to become better in a range of fields of interest, audio analysis will have to play an integral part. Event recognition in autonomous vehicles (AVs) is such a field at a nascent stage that can especially leverage solely on audio or can be part of the multimodal approach. In this manuscript, an extensive analysis focused on the comparison of different magnitude representations of the raw audio is presented. The data on which the analysis is carried out is part of the publicly available MIVIA Audio Events dataset. Single channel Short-Time Fourier Transform (STFT), mel-scale and Mel-Frequency Cepstral Coefficients (MFCCs) spectrogram representations are used. Furthermore, aggregation methods of the aforementioned spectrogram representations are examined; the feature concatenation compared to the stacking of features as separate channels. The effect of the SNR on recognition accuracy and the generalization of the proposed methods on datasets that were both seen and not seen during training are studied and reported.

Audio-Based Event Detection at Different SNR Settings Using Two-Dimensional Spectrogram Magnitude Representations
Ioannis Papadimitriou, Anastasios Vafeiadis, Antonios Lalas, Konstantinos Votis, Dimitrios Tzovaras
Journal: Electronics
Date: 29 September 2020, DOI: 10.3390/electronics9101593
Download: Publisher’s version (Gold Open Access)

New Scientific Article: Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures

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Abstract: Autonomous vehicles (AVs) are already operating on the streets of many countries around the globe. Contemporary concerns about AVs do not relate to the implementation of fundamental technologies, as they are already in use, but are rather increasingly centered on the way that such technologies will affect emerging transportation systems, our social environment, and the people living inside it. Many concerns also focus on whether such systems should be fully automated or still be partially controlled by humans. This work aims to address the new reality that is formed in autonomous shuttles mobility infrastructures as a result of the absence of the bus driver and the increased threat from terrorism in European cities. Typically, drivers are trained to handle incidents of passengers’ abnormal behavior, incidents of petty crimes, and other abnormal events, according to standard procedures adopted by the transport operator. Surveillance using camera sensors as well as smart software in the bus will maximize the feeling and the actual level of security. In this paper, an online, end-to-end solution is introduced based on deep learning techniques for the timely, accurate, robust, and automatic detection of various petty crime types. The proposed system can identify abnormal passenger behavior such as vandalism and accidents but can also enhance passenger security via petty crimes detection such as aggression, bag-snatching, and vandalism. The solution achieves excellent results across different use cases and environmental conditions.

Real-Time Abnormal Event Detection for Enhanced Security in Autonomous Shuttles Mobility Infrastructures
Dimitris Tsiktsiris, Nikolaos Dimitriou, Antonios Lalas, Minas Dasygenis, Konstantinos Votis, Dimitrios Tzovaras
Journal: Sensors
Date: 1 September 2020, DOI: 10.3390/s20174943
Download: Publisher’s version (Gold Open Access)