ST7. 3.1 UC_Electrical and functional integration of high dynamic e-drive system controls with time based vehicle control algorithm 39
ST7. 3.3 UC_Programming paradigms and SW architecture for embedded multi core hybrid powertrain and e-Drive control systems 39
ST7. 1.1 UC_Requirements and specification This subtask contributes the requirements and specification to the overall D7.1 deliverable.
ST7. 1.2 UC_Define system architecture The use case is evolved around a vehicle-centric approach where new ways of communication allow for interaction with the infrastructure and a backoffice. The figure below gives a high-level representation. The progress within communication technology leads to new ways to use and partition ADAS systems. Figure 7: High level representation of system architecture of use case “ADAS and C2X“ Although cellular communication in vehicles is becoming prevalent today, many of the major European OEMs have reached consensus to deliver WLan-P based G5 communication from 2016 (in the context of the Car2Car Communication Consortium). As with any technology migration although penetration will initially be low the technology will become pervasive, leading to new challenges about how to use and rely on V2X data and how to achieve maxiumum Quality of Service in the presence of many vehicles and other wireless communications. The adoption of V2X can further by accelerated by provisioning it into infrastructure elements (to act as virtual sensors) such as at roadworks and at dangerous junctions and by offering after-market units to allow existing deployed fleet to use the V2X facilities. This task studies the implications on the overall system architecture, and delivers a system architecture that leverages in the best way on these anticipated trends.
ST7. 1.5 UC_Set-up and execute trials This task takes care of setting-up and executing different trials to gather hands-on experience. In the Netherlands, the national field operational test (FOT) “Beter Benutten” will start soon. In this FOT a fleet of cars are available for instrumentation and longer term field measurements. Whenever possible, this Living Lab will leverage on this opportunity. In addition, TNO has 5 highly instrumented cars (so-called “carlabs”) that are instrumented with synchronized loggers for CAN, RTK-DGPS and accuracte 6DOF motion sensors. One of more of these carlabs will be used to implement the more demanding applications. Figure 9: Instrumentation of TNO carlabs vehicle 5.3.4 ST7.1.6 UC_Methodology This sub-task is concerned with the elements of the migration of ‘traditional’ vehicle soft systems to next generation EMC2 compliant ones. Contributions:
ST7. 2.4 Situation awareness and HMI In this sub-task, an overall dynamic local map will be generated from the onboard perceived entities and those received from the V2X communications. It will be the basis to derive a risk assessment considering estimated intentions and expectations of other road users, and for further decision-making. In the final step of the information flow, the driving situation interpretation will be properly shown to the driver in order to increase its situation awareness in case of take-over. This objective can be decomposed in the following activities Evaluation of resources allocation mechnisms for the generation of a Probabilistic Dynamic Local Map where several graceful degradation modes are to be considered. Exploitation of parallelization possibilities for risk assessment approaches based on Bayesian Dynamic Networks. Investigation of the level of affordable complexity for the Human- Machine Interface within the proposed Service-oriented architecture where safety critical tasks (interaction) have to coexist with tasks of a lower level of criticality (3D map representation). This sub-task contributes to the common WP7 deliverable (D7.2), common T7.2 deliverables (D7.5 and D7.6) and an internal report for Situation awareness systems and HMI. Relation to business needs: BN_WP7_T7.2_02, BN_WP7_T7.2_05, BN_WP7_T7.2_06 Relation to WPs: WP1. WP3, WP5 and other Use Cases of the Automotive Living Labs
ST7. 2.5 Navigation and Decision-making This sub-task will be in charge of developing the decision system for the supervised automated driving when making complex maneuvers, by taking into account surrounding vehicles and infrastructure information. From the information provided by onboard localization and perception systems (ST7.2.2), and the risk assessment of the scene (ST7.2.3), the implemented decision system will be able to plan and to execute the safest and more efficient strategy within a human-like driving context. In addition, advanced services based on vehicles connectivity like Dynamic ridesharing (DR) and Intelligent Parking Management (IPM) will be evaluated in conjunction with the highly automated driving experience. The main activities to be pursued within the sub-task are the following Introduce reactive Motion Planning considering uncertainties to take advantage of the Bayesian framework used all through the data flow of the subsystems developed in ST7.2.3 and ST7.2.3. Evaluate mixed criticalities handling by running self-Optimization and On-line Learning tasks within the same functional component in charge of the Decision System, which is intrinsically time-critical. Investigate emergency human-in-the-loop control strategies to properly allow the transition between human driving and automated driving Implement through a traffic simulator DR and IPM functionalities and evaluate its proper integration with the embedded navigation system and the HMI. This sub-task contributes to the common WP7 deliverable (D7.2), common T7.2 deliverables (D7.5 and D7.6) and an internal report describing Navigation and Decision-making systems. Relation to business needs: BN_WP7_T7.2_02, BN_WP7_T7.2_05, BN_WP7_T7.2_06 Relation to WPs: WP1. WP3, WP5 and other Use Cases of the Automotive Living Labs
ST7. 3.1 UC_Electrical and functional integration of high dynamic e-drive system controls with time based vehicle control algorithm Multi-core processor systems offer a substantial potential for integrating diverse calculation tasks /calculationalgorithms on one computing unit which are currently executed on two different controller units. This is especially appealing for strongly cost-driven automotive industry as it enables the economization of one embedded controller unit. The development target of this task therefore is a cost-efficient, robust and reliable runtime environment for electric powertrains in HEV-, PHEV and EV-applications which combines the functionality of an E- Motor Controller Unit (EMCU) with the Vehicle Controller Unit (VCU) in one automotive controller unit (VEMCU). By investigating the capabilities of the multi-core technology in one concrete application a bottom-up approach is followed, as the results acquired will be integrated in generally valid functional design guidelines. Goals of this task are to provide requirements and specifications for the application to perform / support integration of the different tailored technologies (outcomes from the other tasks) to perform evaluation of the proposed EMC² technologies Relation to business needs: BN_T7.3_01, BN_T7.3_03, BN_T7.3_04, BN_T7.3_05 Relation to WPs: WP1, WP3, WP4 7.2.2 ST7.3.2 UC_Model-driven system / software / hardware / safety engineering for embedded multi core hybrid powertrain and e-Drive control systems Goal of this task is to enhance model-driven systems engineering approaches in order (1) to better take into account the multi core aspects during system / software / hardware / safety engineering, (2) propose seamless (architecture) modeling approaches to minimize the specification gap between the different disciplines, and (3) improve the links between specification and development tasks. This work covers the following aspects Enhancement, tailoring and integration of semi-formal system / architecture description languages (e.g., SysML, XXXXX Meta Model, EAST-ADL, AUTOSAR) in order to provide a unified framework for the seamless specification of embedded systems Efficient integration of integrated safety mechanisms into an overall concept. This includes the seamless development of the safety concept as well as its verification (dedicated analysis) and validation (test). Developing an automotive domain ontology to reveal the semantics of the interaction betwe...
ST7. 4.1 UC_Requirements and specification of an ACC system architecture for advanced vehicles The task will be devoted to the definition of the requirements and to the specification of ACC system architecture for advanced vehicles derived from the available state of art.
ST7. 4.4 UC_Evaluation The modelled architecture will be refined according to the functional safety requirements and the safety goals derived from the hazard analysis and risk assessment will be validated according to the validation criteria. The evaluation environment will be again the semi-formal language context and its framework, in which the process has been deployed. The methodology will provide a way for the safety requirements evaluation/testing. Relation to business needs: BN_WP7_T7.4_01 - BN_WP7_T7.4_03 Relation to WPs: (WP2), WP5, (WP6)
