Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models

Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we anticipate driving maneuvers a few seconds before they occur. For this purpose we equip a car with cameras and a computing device to capture the driving context from both inside and outside of the car. We propose an Autoregressive Input-Output HMM to model the contextual information alongwith the maneuvers. We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3.5 seconds before they occur with over 80\% F1-score in real-time.Comment: ICCV 2015, http://brain4cars.co

Designing computational grids using best practices in software architechture

The basic principle of sharing and collaborative work by geographically separated computers is known by several names such as meta computing, scalable computing, cluster computing, internet computing and this has today metamorphosed into a new term known as grid computing. Grid computing is proving to be promising method of HPC, which is packaged with many challenges. This paper elucidates the role that pattern can play in architecting complex systems with specific reference to grid computing. We provide descriptions of a set of well-engineered patterns that the practicing developer can apply to crafting his or her own specific applications. We develop the Software Requirements Specification (SRS), with an attempt to drive to effectual design specifications for use by any grid developer. We analyze the grid using an Object Oriented approach and present the design using the unified Modeling Language (UML) which itself helps the identification of patterns at different phases

Designing Computational Grids Using Best Practices in Software Architecture

The basic principle of sharing and collaborative work by geographically separated computers is known by several names such as meta computing, scalable computing, cluster computing, internet computing and this has today metamorphosed into a new term known as grid computing. Grid computing is proving to be promising method of HPC, which is packaged with many challenges. This paper elucidates the role that pattern can play in architecting complex systems with specific reference to grid computing. We provide descriptions of a set of well-engineered patterns that the practicing developer can apply to crafting his or her own specific applications. We develop the Software Requirements Specification (SRS), with an attempt to drive to effectual design specifications for use by any grid developer. We analyze the grid using an Object Oriented approach and present the design using the unified Modeling Language (UML) which itself helps the identification of patterns at different phases

Density-Matrix approach to a Strongly Coupled Two-Component Bose-Einstein Condensate

The time evolution equations for average values of population and relative phase of a strongly coupled two component BEC is derived analytically. The two components are two hyper-fine states coupled by an external laser that drives fast Rabi oscillations between these states. Specifically, this derivation incorporates the two-mode model proposed in [1] for the strongly coupled hyper-fine states of Rb. The fast Rabi cycle is averaged out and rate equations are derived that represents the slow dynamics of the system. These include the collapse and revival of Rabi oscillations and their subsequent dependence on detuning and trap displacement as reported in experiments of [1]. A proposal to create stable vortices is also given.Comment: 11 Latex pages, 2 figures (Figure 3 was removed and the text chnaged accordingly

Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation

Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the interactive hand-tuning of a reward function by RL experts. On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obtained human demonstrations. Yet, IRL suffers from two major limitations: 1) reward ambiguity - there are an infinite number of possible reward functions that could explain an expert's demonstration and 2) heterogeneity - human experts adopt varying strategies and preferences, which makes learning from multiple demonstrators difficult due to the common assumption that demonstrators seeks to maximize the same reward. In this work, we propose a method to jointly infer a task goal and humans' strategic preferences via network distillation. This approach enables us to distill a robust task reward (addressing reward ambiguity) and to model each strategy's objective (handling heterogeneity). We demonstrate our algorithm can better recover task reward and strategy rewards and imitate the strategies in two simulated tasks and a real-world table tennis task.Comment: In Proceedings of the 2020 ACM/IEEE In-ternational Conference on Human-Robot Interaction (HRI '20), March 23 to 26, 2020, Cambridge, United Kingdom.ACM, New York, NY, USA, 10 page

Tandem learning: A learning framework for document categorization

Supervised machine learning techniques rely on the availability of ample training data in the form of labeled instances. However, in text, users can have a strong intuition about the relevance of features, that is, words that are indicative of a topic. In this work we show that user prior knowledge on features is useful for text classification, a domain with many irrelevant and redundant features. The benefit of feature selection is more pronounced when the objective is to learn a classifier with as few training examples as possible. We will demonstrate the role of feature feedback in training a classifier to suitable performance quickly. We find that aggressive feature feedback is necessary to focus the classifier during the early stages of active learning by mitigating the Hughes phenomenon. We will describe an algorithm for tandem learning that begins with a couple of labeled instances, and then at each iteration recommends features and instances for a user to label. The algorithm contains methods to incorporate feature feedback into Support Vector Machines. We design an oracle to estimate an upper bound on tandem learning performance. Tandem learning using an oracle results in much better performance than learning on only features or only instances. We find that humans can emulate the oracle to an extent that results in performance (accuracy) comparable to that of the oracle. Our unique experimental design helps factor out system error from human error, leading to a better understanding of when and why interactive feature selection works from a user perspective. We also design a set of difficulty measures that capture the inherent instance and feature complexity of a problem. We verify the robustness of our measures by showing how instance and feature complexity are highly correlated. Our complexity measures serve as a tool to understand when tandem learning is beneficial for text classification