End-to-end Driving via Conditional Imitation Learning

Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at https://youtu.be/cFtnflNe5fMComment: Published at the International Conference on Robotics and Automation (ICRA), 201

Ontology based Scene Creation for the Development of Automated Vehicles

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.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10 figure

Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving

Tactical decision making for autonomous driving is challenging due to the diversity of environments, the uncertainty in the sensor information, and the complex interaction with other road users. This paper introduces a general framework for tactical decision making, which combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The method is based on the AlphaGo Zero algorithm, which is extended to a domain with a continuous state space where self-play cannot be used. The framework is applied to two different highway driving cases in a simulated environment and it is shown to perform better than a commonly used baseline method. The strength of combining planning and learning is also illustrated by a comparison to using the Monte Carlo tree search or the neural network policy separately

Autonomous power system brassboard

The Autonomous Power System (APS) brassboard is a 20 kHz power distribution system which has been developed at NASA Lewis Research Center, Cleveland, Ohio. The brassboard exists to provide a realistic hardware platform capable of testing artificially intelligent (AI) software. The brassboard's power circuit topology is based upon a Power Distribution Control Unit (PDCU), which is a subset of an advanced development 20 kHz electrical power system (EPS) testbed, originally designed for Space Station Freedom (SSF). The APS program is designed to demonstrate the application of intelligent software as a fault detection, isolation, and recovery methodology for space power systems. This report discusses both the hardware and software elements used to construct the present configuration of the brassboard. The brassboard power components are described. These include the solid-state switches (herein referred to as switchgear), transformers, sources, and loads. Closely linked to this power portion of the brassboard is the first level of embedded control. Hardware used to implement this control and its associated software is discussed. An Ada software program, developed by Lewis Research Center's Space Station Freedom Directorate for their 20 kHz testbed, is used to control the brassboard's switchgear, as well as monitor key brassboard parameters through sensors located within these switches. The Ada code is downloaded from a PC/AT, and is resident within the 8086 microprocessor-based embedded controllers. The PC/AT is also used for smart terminal emulation, capable of controlling the switchgear as well as displaying data from them. Intelligent control is provided through use of a T1 Explorer and the Autonomous Power Expert (APEX) LISP software. Real-time load scheduling is implemented through use of a 'C' program-based scheduling engine. The methods of communication between these computers and the brassboard are explored. In order to evaluate the features of both the brassboard hardware and intelligent controlling software, fault circuits have been developed and integrated as part of the brassboard. A description of these fault circuits and their function is included. The brassboard has become an extremely useful test facility, promoting artificial intelligence (AI) applications for power distribution systems. However, there are elements of the brassboard which could be enhanced, thus improving system performance. Modifications and enhancements to improve the brassboard's operation are discussed