Advanced Driver Assistant Systems (ADAS) use a multitude of input signals for tasks like trajectory planning and control of vehicle dynamics provided by a large variety of information sources such as ...sensors and digital maps. To assure the feature's valid behavior all realistically possible environmental situations have to be tested. The test scenarios used for simulation can be derived from real-worlddriving-data. However, the significance of derived scenarios is weakened by repetitive similar situations within the driving data, which increase the test efforts without providing new insights regarding the test of the ADAS. In this contribution, an automated selection algorithm for test scenarios based on relevant environmental parameters is presented. Starting with a randomly selected initial testset, the machine-learning concept of autoencoders is utilized to recognize novel scenarios within the data pool, which are iteratively added to the initial testset. Furthermore, the key parameters for the autoencoder's performance are shown in depths. The approach is fully automated, so that the identified novel scenarios within an entire testset are automatically combined to a reduced testset of unique relevant scenarios. The achieved testset reduction and thereby the saving potential in simulation time is demonstrated on a dataset including several thousand test kilometers.
Predictive control is a popular approach for further improving the efficiency and performance of vehicular systems enabling intelligent systems behavior appropriate to the driving situation. To ...calculate such control strategies, the future vehicle dynamics or subsequent states have to be predicted. We introduce a stochastic framework based on an explanatory model and stochastic processes to predict future vehicle dynamics with road network data. The distributions of the future states are approximated using sequential Monte Carlo simulation. The proposed approach enables stochastic forecasts incorporating uncertain driver behavior and available road data. Parameter inference is shown for exemplary real-drive test data, and predictive performance is evaluated using commonly used reference models. The results show that the explanatory model provides more specific information than time-series models do, still considering the uncertainty in the driver's behavior or the situation. The framework can be applied with predictive control algorithms enabling intelligent control of vehicular systems. Furthermore, the framework or parts of it may be usable for other applications like predicting behavior of traffic participants or general characterization of driver behavior.
Automated driving is one of the main drivers in the automotive industry. On the way to full automation current Advanced Driver Assistant Systems (ADAS) and Automated Driving Systems (ADS) backed by ...new and enhanced sensor systems take over more and more driving tasks. Developers and engineers are challenged with the increasing Operational Design Domain (ODD) of their systems. The application of these systems has become a daunting task, as a manifold of new situations has to be covered. The number of application parameters has skyrocketed and their scopes are intertwined and not always visible to the vehicle's driver. Virtual, simulation based approaches are on the rise to give developers another tool for the application of their systems. In order to retrieve valid evaluations from the simulation, current research focuses on objectifying the subjective passenger assessments that so far were only conceivable in real world driving tests. Furthermore, with objective and comparable simulation results on statistically significant data samples, application parameters whose effects are not clearly visible in real world driving tests can now be applied systematically and justified. We propose a process reference model for the virtual application of predictive control features with a clear focus on the quality and representativity of the virtual application.
There are several applications that need far trajectory planning within optimal control problems. One use case is the optimal predictive control of plug-in hybrid electric vehicles (PHEV). It is ...possible to find an optimal control with models of the vehicle and environment and a fast optimization algorithm that is capable to calculate over long distances within seconds so that dynamic information such as traffic can be recognized quickly. In this paper, several methods based on dynamic programming (DP) are combined to generate approximated optimal control trajectories with a reduced computational complexity to achieve close-to-real-time application. The resulting trajectories are transferred as strategic planning trajectory to subordinated vehicle controllers. Close-to-optimal trajectories are achieved with a large reduction in memory.
Enhanced capabilities of sensors and digital maps for intelligent vehicles lead to a complex and multivariant system environment with a broad variety of situations and traffic scenarios. To assure ...the feature under development's valid behavior, the sample of scenarios evaluated for Verification and Validation (V&V) needs to proof substantial coverage of all possible situations. Currently applied V&V activities on system-level are in a large part based on real world tests. These are not scalable to sufficiently cover the variant system environment. Our previously introduced Reactive-Replay enables substantial coverage by reuse of recorded real world data in closed-loop simulation. In this contribution we present an approach to determine the relevance of recorded scenarios and derive efficient sets of test scenarios. Our two-step approach starts with a specification-based classification-tree for initial scenario selection. A data-driven reduction of the initial scenario set is achieved by the following analysis of covered parameter spaces. The final consolidated test set avoids repetitive situations while ensuring a significant diversity of the sampled system environment.
As more US shareholders invest in foreign companies, and as more foreign companies seek to raise capital in the US, potential conflicts arise between foreign and domestic securities laws and within ...the US securities laws themselves. While each of the six principal US securities statutes administered by the US Securities and Exchange Commission attempts to address a specific area of securities regulation, these statutes can, in certain instances, overlap. This article examines whether or not shares issued in reliance on the section 3(a)(10) exemption of the 1933 Act can be considered a public offering for purposes of section 7(d) and 3(c)(1) of the 1940 Act. Parts I and II provide brief descriptions of the relevant provisions of both the 1933 Act and the 1940 Act. Part III discusses the relationship between the two Acts, and Part IV analyzes this relationship. Part V concludes with a discussion of the implications of section 3(c)(1) companies being able to - or not being able to - rely on the section 3(a)(10) exemption.
Established methods and processes in the field of Automotive Systems Engineering (ASE) are challenged by the rising complexity of current features. Expanding system boundaries, tighter ...interconnections of functional elements, increasingly complex algorithms and an ever growing operational domain generate a multitude of different scenarios that require consideration during specification, design, implementation and testing. This paper reflects the current practice on the example of the Automotive SPICE process reference for system and software development in the automotive domain. It then contemplates on opportunities of consistent usage of recorded vehicle data throughout all phases of automotive development. Our concept of data-driven development is not intended to replace the current practice but to complement it. A summary of our previous work demonstrates the practicability of the concept on the basis of the development of a Predictive Cruise Control (PCC) feature. The contribution concludes with a scalable concept for the large scale application of data-driven development in ASE.
In this paper we present an online learning approach to predict driver behavior and resulting vehicle states. The driver is represented by driver states x→ and a control function f c . Kernel Density ...Estimation is used for online estimation of current driver states. Data sampling methods are introduced to observe the virtual driver states. The driver states are used as input for the control function to predict resulting vehicle states. To consider environmental influence on driver behavior a context-separated learning approach is presented. The system is tested with real drive test data from different drivers on a specified test route. Different settings regarding learning speed and type of context-separation are investigated. Results show, that consideration of environmental influences on the driver states lead to a better identification of the current behavior but prediction on a longer time horizon does not necessarily improve correspondingly.
Hybrid electric vehicles (HEV) combine both a powerful engine and the ability to reduce fuel consumption through an electric machine by intelligent control of a complex drive train. The driving ...strategy controls the speed and the operating strategy controls the energy management of the HEV. Both are combined in one optimization. The result is a driver assistance system that is tuned with predictive information about the upcoming route. A novel algorithm based on dynamic programming (DP) is presented that allows real-time application by introducing heuristic model-based optimization space reductions. The main focus is on the design and dimensioning of a slim DP algorithm with the capability to find an optimal control in close real-time.
