PEGASUS Project Family Perspective – Supporting the release of Automated Driving in Europe

Homologation relevant building bricks originating from the Pegasus Project Family will be presented focusing on available results, how they fit together and resulting open issues. In detail, a generic simulation architecture and standardization efforts as contributions from SET LEVEL and safety argumentation approaches from VVM as achievements on the way towards the release of automated vehicles in Europe will be shown

Towards Highly Automated Driving: Intermediate report on the HAVEit-Joint System

International audienceThis overview article describes the goals, concepts and very preliminary results of the subproject Joint System within the EU-project HAVEit. The goal of HAVEit is to develop and investigate vehicle automation beyond ADAS systems, especially highly automated driving, where the automation is doing a high percentage of the driving, while the driver is still meaningfully involved in the driving task. In HAVEit, an overarching architecture and several prototypes will be built up over time by manufacturers and suppliers. As a trail blazer, a Joint System prototype is under development by an interdisciplinary team of several European research institutes in order to investigate and demonstrate the basic principles of highly automated driving, which will then be gradually applied to vehicles closer to serial production. Starting with sensor data fusion, the Co-System part of the Joint Systems plans manoeuvres and trajectories, which are then used to control active interfaces and, taking into account the results of an online driver assessment, joined with the actions of the driver. While many aspects of this research undertaking are still under investigation, the concept, a first prototype and first results from a simulator evaluation will be sketched

Driver Monitor and Feedback Dispatcher in SPARC

In context of the EU-project SPARC [1], a comprehensive driver support concept was developed. At first, the actual vehicle behaviour is compared with reference vehicle behaviour, generated by the virtual co-pilot [2]. In case of any deviation the driver will be supported depended on his current condition. With a ‘Driver Monitor’ the system determines to what extent the driver is involved into the actual vehicle guidance. This support is generated by a software module ‘Feedback Dispatcher’ and transmitted as multimodal feedback to the driver

Modeling And Implementation Of Cognitive-Based Supervision and Assistance

This contribution presents firstly the implementation of an automated cognitive-based supervision concept of a real vehicle. The concept employs a Situation-Operator-Modelling (SOM) approach as a representational level to model and formalize the logic of interaction between driver, vehicle and environment based on sensor and video data. The programmed implementation is realized by a Java-Application which can be connected with the ViewCar, the DLR experimental vehicle equipped with specialized sensors and cameras. As an output, the application dsiplays the interpreted situation and all allowed driver actions, which were tested with replay applications on the desktop and in flowing traffic in the ViewCar itself

Das intelligente Auto von Morgen - Trends und Schwerpunkte nter besonderer Berücksichtigung aktueller Softwarekonzepte

Vision des künftigen intelligenten Fahrzeugs und forschungsleitende Fragen an das intelligente Fahrzeu

LiDAR SLAM Positioning Quality Evaluation in Urban Road Traffic

This paper addresses the positioning quality of LiDAR-based Simultaneous Localization And Mapping (SLAM) within urban road traffic. Based on the assumption of functional capability of existing SLAM implementations, the paper evaluates specific details of urban car drives that arise when SLAM is to be used for automatic car control. In the presented case, LiDAR-based positioning is done with the Google Cartographer software which generates real-time updates that are compared to GNSS reference. The evaluation is done by using own Light Detection And Ranging (LiDAR) sensor recordings from urban driving. Next to the overall GNSS-free path estimation, the paper zooms into some typical situations (e.g. waiting at busy intersection, driving curves) where SLAM might be inaccurate