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20 R. Streiter et al. 1 Introduction to GNSS in Automotive Applications The steadily increasing grade of automation in modern vehicle’s driver assistance and automated driving functions has a considerable impact on the development of all subsystems involved. This applies especially to the functions derived from sensor data fusion and environmental perception, as the compliance to functional safety and false alarm specifications is hard to prove and guarantee. One approach to cope with these demanding requirements is the consistent modelling of the system from a probabilistic perspective, which includes signal-processing operations, the propagation of uncertainties, fusion of heterogeneous sensor data and the representation of the system state. This results in the so-called Bayesian framework as the fundamental basis for advanced signal processing that, if applied consistently, can satisfy false alarm specifications and safety issues by nature. The NAVENTIK project provides a key enabling technology to leverage satellite based navigation for automated maneuvers and safety critical decisions in automotive applications. A combination of innovative low-level probabilistic signal processing algorithms will be implemented within a software defined GNSS receiver, in combination with advanced high-level data fusion approaches in order to derive a confidence estimate that is able to meet any safety requirements in urban areas. There are different approaches that are able to detect multipath affected GNSS observations. An overview of these so-called Multipath Mitigation algorithms is given in [1]. A straightforward approach is identifying multipath by considering digital maps with modelled 3D buildings in order to validate the direct line of sight to each satellite. In the Bayesian framework, this approach is used to predict multipath affected GNSS observations [2]. Another algorithm for determining non-line-of-sight (NLOS) with the help of environmental knowledge is described in [3]. The next group is of approaches is of significant interest as it uses statistic tests and probabilistic filtering for the identification and mitigation of multipath. A known representative in this category is the Receiver Autonomous Integrity Monitoring algorithm (RAIM) or an extension called Probabilistic Multipath Mitigation (PMM) [4]. From a probabilistic perspective, the PMM algorithm is of major importance as it was able to show the benefits regarding the improvement of the integrity in different safety relevant automotive use cases. All these approaches are implemented on observation level and suffer from the GNSS receiver’s proprietary signal pre-processing, which is supposed to be not stringent from a probabilistic point of view. To a huge extent remaining errors of multipath mitigation algorithms can be reduced by a reimplementation of GNSS signal processing under the prerequisite of a probabilistically consistent signal tracking. The idea of signal manipulation on radio level is called Software Defined Radio (SDR). In general, this approach is to substitute signal processing hardware on radio level by a software implementation. This technology is very demanding in terms of computing power and data throughput but has high potential from scientific point of
Probabilistic Integration of GNSS for Safety-Critical Driving … 21 view as it allows a flexible implementation and validation of complex signal processing algorithms. In the field of satellite-based navigation, it is called Software Defined GNSS. Complex algorithms like the a GNSS pseudorange error density tracking using a Dirichlet Process Mixture [5] or a Bayesian approach to multipath mitigation in GNSS receivers where implemented based on this technology. Another statistical signal tracking approach on this level with special consideration of tracking time-delays, amplitudes and phases is described in [6]. A Bayesian multisensor navigation, incorporating pseudorange measurements and a multipath model is presented in [7]. In order to meet these demanding computational requirements the NAVENTIK GNSS receiver will be implemented on an automotive grade system on chip that will be described in detail in the next chapter. The NAVENTIK approach is to guarantee to not to discard any information, especially multimodalities in the measurement space. One major problem from multipath mitigation perspective is the evaluation of the auto correlation function for each satellite and the irreversible assignment of pseudoranges. The proposed approach uses the idea of a signal tracking implementation, as a derivative of probabilistic data association (PDA) in order to deal with the multimodal characteristics and outliers within the measurement space and to resolve these ambiguities within the Bayesian framework [8]. Thus, the NAVENTIK signal-tracking algorithm condenses multipath and NLOS effects as generic system properties and the Bayesian implementation improves the integrity of the system state in multipath and NLOS environments. Figure 1 gives an illustrative idea of the integrity concept for one single point in time and its practical influence to applications are described in this chapter related to use cases. Figure 2 shows the metric of confidence within an urban scenario evolving over time. The blue plot represents the real 2-dimensional position error regarding the ground truth of the validation system. A reference GNSS System Fig. 1 High-level representation of the GNSS confidence level as a basis for safety-critical driving functions. The NAVENTIK GNSS receiver guarantees the vehicle is within the confidence level according to a given false alarm specification
Page 1 and 2: Lecture Notes in Mobility Tim Schul
Page 3 and 4: More information about this series
Page 5 and 6: Editors Tim Schulze VDI/VDE Innovat
Page 7 and 8: vi Preface cross-sectorial roadmap
Page 9 and 10: Steering Committee Mike Babala, TRW
Page 11 and 12: xii Contents Robust Facial Landmark
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