A game theoretical model of traffic with multiple interacting drivers for use in autonomous vehicle development

This paper describes a game theoretical model of traffic where multiple drivers interact with each other. The model is developed using hierarchical reasoning, a game theoretical model of human behavior, and reinforcement learning. It is assumed that the drivers can observe only a partial state of the traffic they are in and therefore although the environment satisfies the Markov property, it appears as non-Markovian to the drivers. Hence, each driver implicitly has to find a policy, i.e. a mapping from observations to actions, for a Partially Observable Markov Decision Process. In this paper, a computationally tractable solution to this problem is provided by employing hierarchical reasoning together with a suitable reinforcement learning algorithm. Simulation results are reported, which demonstrate that the resulting driver models provide reasonable behavior for the given traffic scenarios. © 2016 American Automatic Control Council (AACC)

Game Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems

Autonomous driving has been the subject of increased interest in recent years both in industry and in academia. Serious efforts are being pursued to address legal, technical, and logistical problems and make autonomous cars a viable option for everyday transportation. One significant challenge is the time and effort required for the verification and validation of the decision and control algorithms employed in these vehicles to ensure a safe and comfortable driving experience. Hundreds of thousands of miles of driving tests are required to achieve a well calibrated control system that is capable of operating an autonomous vehicle in an uncertain traffic environment where interactions among multiple drivers and vehicles occur simultaneously. Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help to decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests. In this paper, we present a game theoretic traffic model that can be used to: 1) test and compare various autonomous vehicle decision and control systems and 2) calibrate the parameters of an existing control system. We demonstrate two example case studies, where, in the first case, we test and quantitatively compare two autonomous vehicle control systems in terms of their safety and performance, and, in the second case, we optimize the parameters of an autonomous vehicle control system, utilizing the proposed traffic model and simulation environment. IEE

GS32, a Novel Golgi SNARE of 32 kDa, Interacts Preferentially with Syntaxin 6

Syntaxin 1, synaptobrevins or vesicle-associated membrane proteins, and the synaptosome-associated protein of 25 kDa (SNAP-25) are key molecules involved in the docking and fusion of synaptic vesicles with the presynaptic membrane. We report here the molecular, cell biological, and biochemical characterization of a 32-kDa protein homologous to both SNAP-25 (20% amino acid sequence identity) and the recently identified SNAP-23 (19% amino acid sequence identity). Northern blot analysis shows that the mRNA for this protein is widely expressed. Polyclonal antibodies against this protein detect a 32-kDa protein present in both cytosol and membrane fractions. The membrane-bound form of this protein is revealed to be primarily localized to the Golgi apparatus by indirect immunofluorescence microscopy, a finding that is further established by electron microscopy immunogold labeling showing that this protein is present in tubular-vesicular structures of the Golgi apparatus. Biochemical characterizations establish that this protein behaves like a SNAP receptor and is thus named Golgi SNARE of 32 kDa (GS32). GS32 in the Golgi extract is preferentially retained by the immobilized GST–syntaxin 6 fusion protein. The coimmunoprecipitation of syntaxin 6 but not syntaxin 5 or GS28 from the Golgi extract by antibodies against GS32 further sustains the preferential interaction of GS32 with Golgi syntaxin 6