Abstract-Increasing number of in-vehicle sensors, actuators and controllers involved in novel applications such as autonomous driving, requires new communication technologies to fulfill heterogeneous non-functional requirements such as latency, bandwidth and reliability. Time-Sensitive Networking (TSN) is a set of new standards in development by Institute of Electrical and Electronics Engineers (IEEE) defined to support mixed criticality based on Ethernet technology. This technology has recently raised significant attention of automotive domain. However, the mutual influence of application requirements in relation to TSN standards still remains a complex problem to master. For instance, considering an existing complex automotive network, an engineer has to carefully analyze the possible effects of adding new sensors on other existing critical applications. The network has to be configured such that the fulfilling of all requirements is verified. Targeting this problem, a modeling approach based on Logic Programming (LP) is developed to support more efficient configuration and verification process with focus on in-vehicle TSN networks.
As we move towards higher levels of automation in autonomous driving, we see an increase in functionality that either assists or takes over in both normal and emergency scenarios. These new functionalities can be switched off by the user for personalisation. We aim to recognise mistimed and/or unintended deactivation of vehicle functions, in particular, driver assistance functions (ADAS), at run-time. This will be done in addition to already applied methods at design time. Upon recognition of the occurrence, we propose to inform the user and the original equipment manufacturer (OEM) in order to improve both the future and the current system behaviour and to support development processes. Based on eight customer datasets, we evaluated our approach on a total of 17 state-of-the-art ADAS functions per participant, yielding to a total of 136 runs. We observed that during 24 among them, the user de-activated the functions at least once for more than a few seconds. For 13 of these 24 runs, we were able to detect and flag possible nonnominal behaviour over the full trace.
Identity recognition in a car cabin is a critical task nowadays and offers a great field of applications ranging from personalizing intelligent cars to suit drivers' physical and behavioral needs to increasing safety and security. However, the performance and applicability of published approaches are still not suitable for use in series cars and need to be improved. In this paper, we investigate Human Identity Recognition in a car cabin with Time Series Classification (TSC) and deep neural networks. We use gas and brake pedal pressure as input to our models. This data is easily collectable during driving in everyday situations. Since our classifiers have very little memory requirements and do not require any input data preproccesing, we were able to train on one Intel i5-3210M processor only. Our classification approach is based on a combination of LSTM and ResNet. The network trained on a subset of NUDrive outperforms the ResNet and LSTM models trained solely by 35.9 % and 53.85 % accuracy respectively. We reach a final accuracy of 79.49 % on a 10-drivers subset of NUDrive and 96.90 % on a 5-drivers subset of UTDrive.
The growing number of intelligent components inside a car leads to a considerable increase in amount of the produced data. Context aware paradigm plays a major role in managing this data and offering a numerous number of prospects and advantages for existing and new intelligent applications inside the car. Following that, enabling context prediction promises reliable solutions in terms of enhancing the comfort of the occupants and vehicle dynamics. Moreover, this would be a great step toward facilitating highly automated and autonomous driving. However, due to the complex nature of the data resources in an intelligent car and also the lack of comprehensive studies on different aspects of this concept in automotive, defining a functional architecture for context prediction requires broad knowledge and better understanding of multiple domains which are involved and have impacts. In this paper, we investigate the most effective elements and factors in each one of the related domains which help to enable context prediction architectures inside the intelligent cars and analyze the feasible dimensions in detail, cover their advantages, and address the challenges ahead. We elucidate the possibility and validity of our considerations with the help of two use cases of adaptive HVAC and ACC systems.
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