Author name cluster

Abhishek Thakur

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

EAAI Journal 2024 Journal Article

An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles

Abhishek Thakur
Sudhansu Kumar Mishra

This paper delivers an exhaustive analysis of the fusion of multi-sensor technologies, including traditional sensors such as cameras, Light Detection and Ranging(LiDAR), Radio Detection and Ranging(RADAR), and ultrasonic sensors, with Artificial Intelligence(AI) powered methodologies in obstacle detection for Autonomous Vehicles(AVs). With the growing momentum in AVs adoption, a heightened need exists for versatile and resilient obstacle detection systems. Our research delves into study of literatures, where proposed approaches assimilate data from this diverse sensor suite, integrated through Deep Learning(DL) techniques, to refine AV performance. Recent advancements and prevailing challenges within the domain are thoroughly examined, with particular focus on the integration of sensor fusion techniques, the facilitation of real-time processing via edge and fog computing, and the implementation of advanced artificial intelligence architectures, including Convolutional Neural Networks(CNNs), Recurrent Neural Networks(RNNs), and Generative Adversarial Networks(GANs), to enhance data interpretation efficacy. In conclusion, the paper underscores the critical contribution of multi-sensor arrays and deep learning in enhancing the safety and reliability of autonomous vehicles, offering significant perspectives for future research and technological progress.

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AAMAS Conference 2021 Conference Paper

On Teammate-Pattern-Aware Autonomy

Edmund H. Durfee
Abhishek Thakur
Eli Goldweber

We describe an approach for constraining robot autonomy based on the robot’s awareness of patterns of its human teammates’ behaviors, rather than either ignoring its teammates (which is fast but dangerous) or inferring their plans (which is safer but slow). We evaluate this approach in a series of simulated problems where an unmanned ground vehicle and its human teammates must rapidly respond to a sudden context shift, and identify conditions that should be (purposely) met such that a pattern-aware approach is particularly effective compared to the alternatives.

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NeurIPS Conference 2021 Conference Paper

RAFT: A Real-World Few-Shot Text Classification Benchmark

Neel Alex
Eli Lifland
Lewis Tunstall
Abhishek Thakur
Pegah Maham
C. Riedel
Emmie Hine
Carolyn Ashurst

Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? Existing benchmarks are not designed to measure progress in applied settings, and so don't directly answer this question. The RAFT benchmark (Real-world Annotated Few-shot Tasks) focuses on naturally occurring tasks and uses an evaluation setup that mirrors deployment. Baseline evaluations on RAFT reveal areas current techniques struggle with: reasoning over long texts and tasks with many classes. Human baselines show that some classification tasks are difficult for non-expert humans, reflecting that real-world value sometimes depends on domain expertise. Yet even non-expert human baseline F1 scores exceed GPT-3 by an average of 0. 11. The RAFT datasets and leaderboard will track which model improvements translate into real-world benefits at https: //raft. elicit. org/.

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JAAMAS Journal 2020 Journal Article

Teammate-pattern-aware autonomy based on organizational self-design principles

Edmund H. Durfee
Abhishek Thakur
Eli Goldweber

Abstract We describe an approach for constraining robot autonomy based on the robot’s awareness of patterns of its human teammates’ behaviors, rather than either ignoring its teammates (which is fast but dangerous) or inferring their plans (which is safer but slow). We explore the promise, and limitations, of this approach in a series of simulated problems where an unmanned ground vehicle and its human teammates must rapidly respond to a sudden context shift. Our results help us discern conditions under which a pattern-aware approach can be more effective than the alternatives, and our current efforts investigate how the manned–unmanned team can adopt biases to more readily establish such conditions that are more favorable to the pattern-aware approach.

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