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Register a new userVector-based Pedestrian Navigation in Cities
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How do pedestrians choose their paths within city street networks? Human path planning has been extensively studied at the aggregate level of mobility flows, and at the individual level with strictly designed behavioural experiments. However, a comprehensive, individual-level model of how humans select pedestrian paths in real urban environments is still lacking. Here, we analyze human path planning behaviour in a large dataset of individual pedestrians, whose GPS traces were continuously recorded as they pursued their daily goals. Through statistical analysis we reveal two robust empirical discoveries, namely that (1) people increasingly deviate from the shortest path as the distance between origin and destination increases, and (2) individual choices exhibit direction-dependent asymmetries when origin and destination are swapped. In order to address the above findings, which cannot be explained by existing models, we develop a vector-based navigation framework motivated by the neural evidence of direction-encoding cells in hippocampal brain networks, and by behavioural evidence of vector navigation in animals. Modelling pedestrian path preferences by vector-based navigation increases the models predictive power by 35%, compared to a model based on minimizing distance with stochastic effects. We show that these empirical findings and modelling results generalise across two major US cities with drastically different street networks, suggesting that vector-based navigation is a universal property of human path planning, independent of specific city environments. Our results offer a simple, unified explanation of numerous findings about human navigation, and posit a computational mechanism that may underlie the human capacity to efficiently navigate in environments at various scales.
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In this paper, we propose a novel navigation system for mobile robots in pedestrian-rich sidewalk environments. Sidewalks are unique in that the pedestrian-shared space has characteristics of both roads and indoor spaces. Like vehicles on roads, pedestrian movement often manifests as linear flows in opposing directions. On the other hand, pedestrians also form crowds and can exhibit much more random movements than vehicles. Classical algorithms are insufficient for safe navigation around pedestrians and remaining on the sidewalk space. Thus, our approach takes advantage of natural human motion to allow a robot to adapt to sidewalk navigation in a safe and socially-compliant manner. We developed a textit{group surfing} method which aims to imitate the optimal pedestrian group for bringing the robot closer to its goal. For pedestrian-sparse environments, we propose a sidewalk edge detection and following method. Underlying these two navigation methods, the collision avoidance scheme is human-aware. The integrated navigation stack is evaluated and demonstrated in simulation. A hardware demonstration is also presented.
We present a new microscopic ODE-based model for pedestrian dynamics: the Gradient Navigation Model. The model uses a superposition of gradients of distance functions to directly change the direction of the velocity vector. The velocity is then integrated to obtain the location. The approach differs fundamentally from force based models needing only three equations to derive the ODE system, as opposed to four in, e.g., the Social Force Model. Also, as a result, pedestrians are no longer subject to inertia. Several other advantages ensue: Model induced oscillations are avoided completely since no actual forces are present. The derivatives in the equations of motion are smooth and therefore allow the use of fast and accurate high order numerical integrators. At the same time, existence and uniqueness of the solution to the ODE system follow almost directly from the smoothness properties. In addition, we introduce a method to calibrate parameters by theoretical arguments based on empirically validated assumptions rather than by numerical tests. These parameters, combined with the accurate integration, yield simulation results with no collisions of pedestrians. Several empirically observed system phenomena emerge without the need to recalibrate the parameter set for each scenario: obstacle avoidance, lane formation, stop-and-go waves and congestion at bottlenecks. The density evolution in the latter is shown to be quantitatively close to controlled experiments. Likewise, we observe a dependence of the crowd velocity on the local density that compares well with benchmark fundamental diagrams.
Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic noise and systematic changes in the animals cognitive and behavioral state. In addition to investigating how noise and state changes impact neural computation, statistical models of trial-to-trial variability are becoming increasingly important as experimentalists aspire to study naturalistic animal behaviors, which never repeat themselves exactly and may rarely do so even approximately. Estimating the basic features of neural response distributions may seem impossible in this trial-limited regime. Fortunately, by identifying and leveraging simplifying structure in neural data -- e.g. shared gain modulations across neural subpopulations, temporal smoothness in neural firing rates, and correlations in responses across behavioral conditions -- statistical estimation often remains tractable in practice. We review recent advances in statistical neuroscience that illustrate this trend and have enabled novel insights into the trial-by-trial operation of neural circuits.
Most studies indicate that intelligence (g) is positively correlated with cortical thickness. However, the interindividual variability of cortical thickness has not been taken into account. In this study, we aimed to identify the association between intelligence and cortical thickness in adolescents from both the groups mean and dispersion point of view, utilizing the structural brain imaging from the Adolescent Brain and Cognitive Development (ABCD) Consortium, the largest cohort in early adolescents around 10 years old. The mean and dispersion parameters of cortical thickness and their association with intelligence were estimated using double generalized linear models(DGLM). We found that for the mean model part, the thickness of the frontal lobe like superior frontal gyrus was negatively related to intelligence, while the surface area was most positively associated with intelligence in the frontal lobe. In the dispersion part, intelligence was negatively correlated with the dispersion of cortical thickness in widespread areas, but not with the surface area. These results suggested that people with higher IQ are more similar in cortical thickness, which may be related to less differentiation or heterogeneity in cortical columns.
Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. Recently, there has been a growing interest in applying deep neural networks (DNNs) to motion sensing and location estimation. However, the lack of sufficient labelled data for training and evaluating architecture benchmarks has limited the adoption of DNNs in IMU-based tasks. In this paper, we present and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research, with fine-grained ground-truth on all sequences. Furthermore, to enable more efficient inference at the edge, we propose a novel lightweight framework to learn and reconstruct pedestrian trajectories from raw IMU data. Extensive experiments show the effectiveness of our dataset and methods in achieving accurate data-driven pedestrian inertial navigation on resource-constrained devices.
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