Predictive positioning and quality of service ridesharing for campus mobility on demand systems

Autonomous Mobility On Demand (MOD) systems can utilize fleet management strategies in order to provide a high customer quality of service (QoS). Previous works on autonomous MOD systems have developed methods for rebalancing single capacity vehicles, where QoS is maintained through large fleet sizing. This work focuses on MOD systems utilizing a small number of vehicles, such as those found on a campus, where additional vehicles cannot be introduced as demand for rides increases. A predictive positioning method is presented for improving customer QoS by identifying key locations to position the fleet in order to minimize expected customer wait time. Ridesharing is introduced as a means for improving customer QoS as arrival rates increase. However, with ridesharing perceived QoS is dependent on an often unknown customer preference. To address this challenge, a customer ratings model, which learns customer preference from a 5-star rating, is developed and incorporated directly into a ridesharing algorithm. The predictive positioning and ridesharing methods are applied to simulation of a real-world campus MOD system. A combined predictive positioning and ridesharing approach is shown to reduce customer service times by up to 29%. and the customer ratings model is shown to provide the best overall MOD fleet management performance over a range of customer preferences.Ford Motor CompanyFord-MIT Allianc

Demand estimation and chance-constrained fleet management for ride hailing

In autonomous Mobility on Demand (MOD) systems, customers request rides from a fleet of shared vehicles that can be automatically positioned in response to customer demand. Recent approaches to MOD systems have focused on environments where customers can only request rides through an app or by waiting at a station. This paper develops MOD fleet management approaches for ride hailing, where customers may instead request rides simply by hailing a passing vehicle, an approach of particular importance for campus MOD systems. The challenge for ride hailing is that customer demand is not explicitly provided as it would be with an app, but rather customers are only served if a vehicle happens to be located at the arrival location. This work focuses on maximizing the number of served hailing customers in an MOD system by learning and utilizing customer demand. A Bayesian framework is used to define a novel customer demand model which incorporates observed pedestrian traffic to estimate customer arrival locations with a quantification of uncertainty. An exploration planner is proposed which routes MOD vehicles in order to reduce arrival rate uncertainty. A robust ride hailing fleet management planner is proposed which routes vehicles under the presence of uncertainty using a chance-constrained formulation. Simulation of a real-world MOD system on MIT's campus demonstrates the effectiveness of the planners. The customer demand model and exploration planner are demonstrated to reduce estimation error over time and the ride hailing planner is shown to improve the fraction of served customers in the system by 73% over a baseline exploration approach.Ford-MIT AllianceFord Motor Compan

Simulation of a novel electromechanical engine valve drive to quantify performance gains in fuel consumption

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 67-68).This thesis describes the modeling and simulation of a novel electromechanical valve drive known as the MIT EMV. This valve drive allows an engine to achieve variable valve timing which has been shown to produce improvements in engine fuel efficiency. To test this improvement, a reference engine model with fixed valve timing was obtained from the engine simulation software package WAVE® by Ricardo. A model of the MIT EMV was generated based on the details of the physical actuator, and it was incorporated into the WAVE® engine model. An interface between MATLAB® and WAVE® was developed for simulating the actuator at desired engine speeds and loads. Specific test points were chosen based on corporate operating points and operating points that were used to test the BMW Valvetronic actuator. Through simulation, it was determined that the MIT EMV can provide a reduction of approximately 10% in fuel consumption at the corporate operating points when compared to the reference engine model. The drive was also able to achieve performance gains similar to the BMW Valvetronic actuator, showing that it is able to compete with other actuators on the market even without variable lift capabilities.by Justin Miller.S.M

Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation

Neural population activity relating to behaviour is assumed to be inherently low-dimensional despite the observed high dimensionality of data recorded using multi-electrode arrays. Therefore, predicting behaviour from neural population recordings has been shown to be most effective when using latent variable models. Over time however, the activity of single neurons can drift, and different neurons will be recorded due to movement of implanted neural probes. This means that a decoder trained to predict behaviour on one day performs worse when tested on a different day. On the other hand, evidence suggests that the latent dynamics underlying behaviour may be stable even over months and years. Based on this idea, we introduce a model capable of inferring behaviourally relevant latent dynamics from previously unseen data recorded from the same animal, without any need for decoder recalibration. We show that unsupervised domain adaptation combined with a sequential variational autoencoder, trained on several sessions, can achieve good generalisation to unseen data and correctly predict behaviour where conventional methods fail. Our results further support the hypothesis that behaviour-related neural dynamics are low-dimensional and stable over time, and will enable more effective and flexible use of brain computer interface technologies

Deconstructing the LGBT-Victimization Association: The Case of Sexual Assault and Alcohol-Related Problems

Research on lesbian, gay, bisexual and/or transgender (LGBT) students has been gaining traction in the fields of criminology, victimology, and education, but available data lag behind the demand for studies on this underserved population. While LGBT students are often perceived to face greater risk of victimization and subsequent health problems than their counterparts, little research has investigated the mechanisms behind problematic outcomes for LGBT students. This research uses data from a Southeastern University to examine sexual assault among LGBT students and their experiences with alcohol-related problems. The results show that LGBT youth are at an increased risk for sexual victimization but that LGBT status does not have a direct effect on alcohol-related problems

Predictive Modeling of Pedestrian Motion Patterns with Bayesian Nonparametrics

For safe navigation in dynamic environments, an autonomous vehicle must be able to identify and predict the future behaviors of other mobile agents. A promising data-driven approach is to learn motion patterns from previous observations using Gaussian process (GP) regression, which are then used for online prediction. GP mixture models have been subsequently proposed for finding the number of motion patterns using GP likelihood as a similarity metric. However, this paper shows that using GP likelihood as a similarity metric can lead to non-intuitive clustering configurations - such as grouping trajectories with a small planar shift with respect to each other into different clusters - and thus produce poor prediction results. In this paper we develop a novel modeling framework, Dirichlet process active region (DPAR), that addresses the deficiencies of the previous GP-based approaches. In particular, with a discretized representation of the environment, we can explicitly account for planar shifts via a max pooling step, and reduce the computational complexity of the statistical inference procedure compared with the GP-based approaches. The proposed algorithm was applied on two real pedestrian trajectory datasets collected using a 3D Velodyne Lidar, and showed 15% improvement in prediction accuracy and 4.2 times reduction in computational time compared with a GP-based algorithm.Ford Motor Compan

Targeted Neural Dynamical Modeling

Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously measured behaviour into these models to further disentangle sources of neural variability in their latent space. These approaches, however, are limited in their ability to capture the underlying neural dynamics (e.g. linear) and in their ability to relate the learned dynamics back to the observed behaviour (e.g. no time lag). To this end, we introduce Targeted Neural Dynamical Modeling (TNDM), a nonlinear state-space model that jointly models the neural activity and external behavioural variables. TNDM decomposes neural dynamics into behaviourally relevant and behaviourally irrelevant dynamics; the relevant dynamics are used to reconstruct the behaviour through a flexible linear decoder and both sets of dynamics are used to reconstruct the neural activity through a linear decoder with no time lag. We implement TNDM as a sequential variational autoencoder and validate it on simulated recordings and recordings taken from the premotor and motor cortex of a monkey performing a center-out reaching task. We show that TNDM is able to learn low-dimensional latent dynamics that are highly predictive of behaviour without sacrificing its fit to the neural data

Filling the BINs of life: Report of an amphibian and reptile survey of the Tanintharyi (Tenasserim) Region of Myanmar, with DNA barcode data

Despite threats of species extinctions, taxonomic crises, and technological advances in genomics and natural history database informatics, we are still distant from cataloguing all of the species of life on earth. Amphibians and reptiles are no exceptions; in fact new species are described nearly every day and many species face possible extinction. The number of described species continues to climb as new areas of the world are explored and as species complexes are examined more thoroughly. The use of DNA barcoding provides a mechanism for rapidly estimating the number of species at a given site and has the potential to record all of the species of life on Earth. Though DNA barcoding has its caveats, it can be useful to estimate the number of species in a more systematic and efficient manner, to be followed in combination with more traditional, morphology-based identifications and species descriptions. Herein, we report the results of a voucher-based herpetological expedition to the Tanintharyi (Tenasserim) Region of Myanmar, enhanced with DNA barcode data. Our main surveys took place in the currently proposed Tanintharyi National Park. We combine our results with photographs and observational data from the Chaung-naukpyan forest reserve. Additionally, we provide the first checklist of amphibians and reptiles of the region, with species based on the literature and museum. Amphibians, anurans in particular, are one of the most poorly known groups of vertebrates in terms of taxonomy and the number of known species, particularly in Southeast Asia. Our rapid-assessment program combined with DNA barcoding and use of Barcode Index Numbers (BINs) of voucher specimens reveals the depth of taxonomic diversity in the southern Tanintharyi herpetofauna even though only a third of the potential amphibians and reptiles were seen. A total of 51 putative species (one caecilian, 25 frogs, 13 lizards, 10 snakes, and two turtles) were detected, several of which represent potentially undescribed species. Several of these species were detected by DNA barcode data alone. Furthermore, five species were recorded for the first time in Myanmar, two amphibians (Ichthyophis cf. kohtaoensis and Chalcorana eschatia) and three snakes (Ahaetulla mycterizans, Boiga dendrophila, and Boiga drapiezii)

Motion planning with diffusion maps

Many robotic applications require repeated, on-demand motion planning in mapped environments. In addition, the presence of other dynamic agents, such as people, often induces frequent, dynamic changes in the environment. Having a potential function that encodes pairwise cost-to-go can be useful for improving the computational speed of finding feasible paths, and for guiding local searches around dynamic obstacles. However, since storing pairwise potential can be impractical given the O(|V|²) memory requirement, existing work often needs to compute a potential function for each query to a new goal, which would require a substantial online computation. This work addresses the problem by using diffusion maps, a machine learning algorithm, to learn the map's geometry and develop a memory-efficient parametrization (O(|V|)) of pairwise potentials. Specially, each state in the map is transformed to a diffusion coordinate, in which pairwise Euclidean distance is shown to be a meaningful similarity metric. We develop diffusion-based motion planning algorithms and, through extensive numerical evaluation, show that the proposed algorithms find feasible paths of similar quality with orders of magnitude improvement in computational speed compared with single-query methods. The proposed algorithms are implemented on hardware to enable real-time autonomous navigation in an indoor environment with frequent interactions with pedestrians.Ford Motor Compan