Abstract. Group selection is easily observed when spatial group structure is imposed on a population. In fact, spatial structure is just a means of providing assortative interactions such that the benefits of cooperating are delivered to other cooperators more than to selfish individuals. In principle, assortative interactions could be supported by individually adapted traits without physical grouping. But this possibility seems to be ruled-out because any 'marker' that cooperators used for this purpose could be adopted by selfish individuals also. However, here we show that stable assortative marking can evolve when sub-populations at different evolutionarily stable strategies (ESSs) are brought into contact. Interestingly, if they are brought into contact too quickly, individual selection causes loss of behavioural diversity before assortative markers have a chance to evolve. But if they are brought into contact slowly, moderate initial mixing between sub-populations produces a pressure to evolve traits that facilitate assortative interactions. Once assortative interactions have become established, group competition between the two ESSs is facilitated without any spatial group structure. This process thus illustrates conditions where individual selection canalises groups that are initially spatially defined into stable groups that compete without the need for continued spatial separation.
This paper focuses on the problem of person detection in harsh industrial environments. Different image regions often have different requirements for the person to be detected. Additionally, as the environment can change on a frame to frame basis even previously detected people can fail to be found. In our work we adapt a previously trained classifier to improve its performance in the industrial environment. The classifier output is initially used an image descriptor. Structure from the descriptor history is learned using semi-supervised learning to boost overall performance. In comparison with two state of the art person detectors we see gains of 10%. Our approach is generally applicable to pretrained classifiers which can then be specialised for a specific scene.
Abstract-An experiment was conducted using the InnovITS proving ground in Nuneaton. Thirty cars with volunteer drivers were asked to drive around a tight closed road circuit causing them to pass repeatedly through a cross-roads junction from all directions. The junction was signalized. In different testruns of the experiment the traffic lights were controlled by either an automated fixed-time system or by a human using remote control. All vehicles in the test were instrumented using GPS and bluetooth. Video footage from two cameras was also recorded.The goal of the experiment was to collect data on the performance of human junction controllers. This was motivated by earlier work indicated that human controllers could perform well at this task in a simulated 'computer game' environment.In particular this paper examines some of the issues that arise when trying to simulate an urban road junction in this manner. For example results are presented indicating differences in network performance depending on whether the drivers were instructed to follow a fixed route or a random route of their choice. Thus providing some guidance for maximizing the fidelity of this type of simulation in the future.The paper also presents a detailed analysis of the sensor data and video footage to measure the performance of the junction under the different modes of control.
The ability to accurately predict driver route choices is an important part of traffic assignment, the process of forecasting traffic flows on roads across a region. Many assignment methods only consider the presence of recurrent forms of congestion, such as during rush hour periods, and fail to incorporate non-recurrent congestion effects caused by irregular events such as road traffic accidents. This paper proposes an agent based driver route choice model which includes driver reactions to the presence of non-recurrent congestion, supposing that drivers learn relationships between congestion locations and adjust their expectation of network travel times en-route, potentially choosing to divert. By simulating an example network with mixed populations consisting of agents capable of diverting and not, the result is found that initially increasing the proportion of diverting agents from zero is beneficial to the system as might be expected, reducing the number of vehicles navigating the incident affected area, but beyond a tipping point agents can no longer perceive the presence of congestion prior to diverting and network performance decreases. The model not only demonstrates the conflict between agents adopting travel time reducing behaviour and its impact on system performance, but it also highlights the importance of modelling driver knowledge appropriately to reproduce plausible phenomena in simulation.
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