The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.
Xiong @cite_24 proposes a framework for for simulation of test cases based on driving scenarios for automated vehicles. Therefore, the framework consists of an ontology for scenario orchestration (OSO), virtual driver(s), a collection of supporting modules in a scenario management module (SMM), and a scenario observer. One of the main concepts is a simulation supervision linked with the driver models, which calculate all interactions between the entities in the simulation and the necessary tools according to the defined scenario depending on the simulation framework. This framework has been evaluated in two simulation platforms by executing multiple predefined scenarios.
@cite_24
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A scenario in a driving simulator covers what the human participants experience and what the researchers need: the physical scene, predefined traffic flow, simulated vehicles' interactions with the participants and measurements to be collected. Current methodologies used to orchestrate scenarios regarding the interactions have the following drawbacks: 1) Action sequences that simulated vehicles should follow in scenarios are specified without the contexts of each Action; 2) programming languages always include platform-dependent details and are not suitable for context modelling and scenario sharing and 3) there is no mechanism to handle scenarios dynamically and deal with failures to deploy a scenario. To overcome these problems, a concept named Assignment, which represents the task(s) of Virtual Drivers, was first developed to encode the contextual information of proposed Actions for interaction generation, e.g., potential simulated vehicles involved. The Ontology for Scenario Orchestration (OSO) was then developed to model concepts and their relationships in the domain of scenario orchestration including the concept Assignment. It can also provide a file for machine processing. An algorithm named NAUSEA (autoNomous locAl manoeUvre and Scenario orchEstration based on automated action plAnning) was finally generated to utilise Assignments recorded in OSO. Encoded in the driver model SAIL (Scenario-Aware drIver modeL), NAUSEA can be used by a Virtual Driver to control simulated vehicles dynamically. Failed interactions, generated by corresponding Assignments, can be regenerated if necessary. A framework SOAV (Scenario Orchestration with Autonomous simulated Vehicles) was formed to support SAIL - NAUSEA and orchestrate scenarios with autonomous vehicles. Three verification experiments were carried out and showed that SOAV was working properly by producing corresponding interactions based on SAIL NAUSEA and Assignments. They also demonstrated that OSO can provide contextual information in a human-readable and machine processable manner. The OSO evaluation showed that OSO has several advantages such as being readable, flexible etc., but how it can be presented to varieties of audiences needs further examination. In this work, a framework for motion prediction of vehicles and safety assessment of traffic scenes is presented. The developed framework can be used for driver assistant systems as well as for autonomous driving applications. In order to assess the safety of the future trajectories of the vehicle, these systems require a prediction of the future motion of all traffic participants. As the traffic participants have a mutual influence on each other, the interaction of them is explicitly considered in this framework, which is inspired by an optimization problem. Taking the mutual influence of traffic participants into account, this framework differs from the existing approaches which consider the interaction only insufficiently, suffering reliability in real traffic scenes. For motion prediction, the collision probability of a vehicle performing a certain maneuver, is computed. Based on the safety evaluation and the assumption that drivers avoid collisions, the prediction is realized. Simulation scenarios and real-world results show the functionality. This paper presents a novel cooperative-driving prediction and planning framework for dynamic environments based on the methods of game theory. The proposed algorithm can be used for highly automated driving on highways or as a sophisticated prediction module for advanced driver-assistance systems with no need for intervehicle communication. The main contribution of this paper is a model-based interaction-aware motion prediction of all vehicles in a scene. In contrast to other state-of-the-art approaches, the system also models the replanning capabilities of all drivers. With that, the driving strategy is able to capture complex interactions between vehicles, thus planning maneuver sequences over longer time horizons. It also enables an accurate prediction of traffic for the next immediate time step. The prediction model is supported by an interpretation of what other drivers intend to do, how they interact with traffic, and the ongoing observation. As part of the prediction loop, the proposed planning strategy incorporates the expected reactions of all traffic participants, offering cooperative and robust driving decisions. By means of experimental results under simulated highway scenarios, the validity of the proposed concept and its real-time capability is demonstrated. This paper proposes an approach to automatically generating test cases for testing motion controllers of autonomous vehicular systems. Test scenarios may consist of single or multiple vehicles under test at the same time. Tests are performed in simulation environments. The approach is based on using a robustness metric for evaluating simulation outcomes as a cost function. Initial states and inputs are updated by stochastic optimization methods between the tests for achieving smaller robustness values. The test generation framework has been implemented in the toolbox S-TaLiRo. The proposed framework's ability to generate interesting test cases is demonstrated by a case study.