Effective simulation and testing environment is a vital part in the research of self-driving vehicles. It is capable of testing self-driving software and hardware quickly in a variety of virtual environments at low cost. However, as for the current mainstream simulation platforms, a considerable gap exists between the constructed virtual environment and the actual self-driving platform, which decreases the efficiency of development and makes it difficult to complete the migration from the virtual scenario to the real environment. Therefore, in this paper, we proposed a novel hardware-in-the-loop (HiL) simulation system. It takes the real hardware electronic control unit (ECU) of the self-driving vehicle as a part of the simulation platform, which improves the efficiency of development and testing, and also, the verified algorithms can be implemented into self-driving cars faster than ever before. The proposed HiL simulation system mainly consists of four parts: the vehicle kinematic model simulation, the multi-sensor simulation, the environment simulation, and the ECU hardware. Simulation experiments on applying the HiL system are used to verify the validity of self-driving algorithms in virtual scenes, including perception, planning, decision making, and control. Furthermore, algorithms that are tested in the simulation environment can be rapidly deployed into the real self-driving vehicles. In this paper, we also presented the verification processes of various algorithms, such as planning and control. These algorithms are implemented in the HiL system, and the experimental results show the validity of our proposed platform.

A Novel Integrated Simulation and Testing Platform for Self-Driving Cars With Hardware in the Loop

Developed societies have a high level of preparedness for natural or man-made disasters. But such incidents cannot be completely prevented, and when an incident like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams need to be employed. In such situations it is a necessity for rescue teams to get a quick overview of the situation in order to identify possible locations of victims that need to be rescued and dangerous locations that need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous environments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is to network the various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the goals and first research results of the ongoing joint research project SENEKA.

SENEKA - sensor network with mobile robots for disaster management

Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of `perception, planning, decision-making, and control. It is necessary to explore a basic computing framework that conforms to human drivers attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence.In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios.The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment.

Autonomous driving: cognitive construction and situation understanding

Throughout the last decades, the Robotics Community has influenced the Autonomous Vehicles field in multiple different areas ranging from Scene Understanding and Decision Making to Vehicle Control and Optimal Path Planning. Existing path planning algorithms such as A-star, Dijkstra and Graph-based approaches, although providing good optimal path approximations, are suffering from high-runtimes to convergence. This paper presents an innovative and computationally efficient approach of fusing the well-known Hybrid A-star search engine with the Visibility Diagram planning to find the shortest possible non-holonomic path in a hybrid (continuous-discrete) environment for valet parking. The primary novelty of our method stems from two points: at first, we use the Visibility Diagram to introduce an improved and application-aware cost function for the Hybrid A-star algorithm, and then the derived shortest path is used to provide the correct waypoints for the Hybrid A-star to plan the optimum path in regard to the non-holonomic constraints. The method has been extensively tested and proven to up to 40% (20% in average) faster than Hybrid A-star algorithm.

Guided Hybrid A-star Path Planning Algorithm for Valet Parking Applications

Thorough robot navigation based on SVM local planning

Efficient path planning is one of the main prerequisites for robust navigation of autonomous robots. Especially driving in complex environments containing both streets and unstructured regions is a challenging problem. In this paper we present the application of the Hybrid A* algorithm to a nonholonomic mobile outdoor robot in order to plan near optimal paths in mostly unknown and potentially intricate environments. The implemented algorithm is capable of generating paths with a rate of at least 10 Hz to guarantee real-time behavior.

Application of Hybrid A* to an Autonomous Mobile Robot for Path Planning in Unstructured Outdoor Environments

The method involves pulling back an air route in direction to a target region (4) by a reconnaissance drone (12), where the target region is provided for a base target engagement. A reconnaissance probe (18) executes an automatic localization for determining their location coordinates using base objects which are included in a preprocessed digital mission map. An independent claim is also included for a system for reconnaissance of a target region.

Method for reconnaissance of target region, particularly landscape, involves pulling back air route in direction to target region by reconnaissance drone, where target region is provided for base target engagement

The method involves controlling mission components during flight of a combat missile by a control unit. Detection data for the mission components are contained in a data storage. Type data is read out from the mission components by the control unit. Types of the mission components are identified based on the detection data and the type data by the control unit. Control steps are executed based on the type of the mission components by the control unit. Flight data of the combat missile is calculated from mission data. An independent claim is also included for a system including multiple combat missiles.

Method for guiding a military missile

Die Erfindung geht aus von einem Verfahren zum Steuern eines Gefechtsflugkorpers (2), der einen Datenspeicher (24), ein Steuermittel (22) und eine Missionskomponente (50) enthalt, bei dem das Steuermittel (22) die Missionskomponente (50) wahrend eines Flugs des Gefechtsflugkorpers (2) steuert. Um eine Anpassung einer Flugkorpersteuerung an eine neue Missionskomponente (50) zu erleichtern, wird vorgeschlagen, dass der Datenspeicher (24) Erkennungsdaten zu einer Mehrzahl verschiedener Missionskomponenten (50) enthalt und das Steuermittel (22) Typdaten aus der Missionskomponente (50) ausliest, den Typ der Missionskomponente (50) anhand der Erkennungsdaten und der Typdaten identifiziert und Steuerschritte in Abhangigkeit vom Typ der Missionskomponente (50) ausfuhrt.

Andreas Beutel

Papers

Application of Hybrid A* to an Autonomous Mobile Robot for Path Planning in Unstructured Outdoor Environments

Method for reconnaissance of target region, particularly landscape, involves pulling back air route in direction to target region by reconnaissance drone, where target region is provided for base target engagement

Method for guiding a military missile

Verfahren zum Steuern eines Gefechtsflugkörpers