Path planning and optimization in the traveling salesman problem: Nearest neighbor vs. region-based strategies

According to the number of targets, route planning can be a very complex task. Human navigators, however, usually solve route planning tasks fastly and efficiently. Here two experiments are presented that studied human route planning performance, route planning strategies employed, and cognitive processes involved. For this, 25 places were arranged on a regular grid in a large room. Each place was marked by a unique symbol. Subjects were repeatedly asked to solve traveling salesman problems (TSP), i.e. to find the shortest closed loop connecting a given start place with a number of target places. For this, subjects were given a so-called \u27shopping list\u27 depicting the symbols of the start place and the target places. While the TSP is computationally hard, sufficient solutions can be found by simple strategies such as the nearest neighbor strategy. In Experiment 1, it was tested whether humans deployed the nearest neighbor strategy (NNS) when solving the TSP. Results showed that subjects outperformed the NNS in cases in which the NNS did not predict the optimal solution, suggesting that the NNS is not sufficient to explain human route planning behavior. As a second possible strategy a region-based approach was tested in Experiment 2. When optimal routes required more region transitions than other, sub-optimal routes, subjects preferred these sub-optimal routes. This result suggests that subjects first planned a coarse route on the region level and then refined the route during navigation. Such a hierarchical planning stragey would allow to reduce computational effort during route planning. In a control condition, the target places were directly marked in the environment rather than being depicted on the shopping list. As subjects did not have to identify and remember the positions of the target places based on the shopping list during route planning, this control condition tested for the influence of spatial working memory for route planning performance. Results showed a strong performance increase in the control condition, emphasizing the prominent role of spatial working memory for route planning

Generalized Analysis of Weakly-Interacting Massive Particle Searches

We perform a generalized analysis of data from WIMP search experiments for point-like WIMPs of arbitrary spin and general Lorenz-invariant WIMP-nucleus interaction. We show that in the non-relativistic limit only spin-independent (SI) and spin-dependent (SD) WIMP-nucleon interactions survive, which can be parameterized by only five independent parameters. We explore this five-dimensional parameter space to determine whether the annual modulation observed in the DAMA experiment can be consistent with all other experiments. The pure SI interaction is ruled out except for very small region of parameter space with the WIMP mass close to 50 GeV and the ratio of the WIMP-neutron to WIMP-proton SI couplings $-0.77\le f_n/f_p\le -0.75$. For the predominantly SD interaction, we find an upper limit to the WIMP mass of about 18 GeV, which can only be weakened if the constraint stemming from null searches for energetic neutrinos from WIMP annihilation the Sun is evaded. None of the regions of the parameter space that can reconcile all WIMP search results can be easily accommodated in the minimal supersymmetric extension of the standard model.Comment: 27 pages, 3 figure

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classi- fier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios