Aamir Hasan

Aamir Hasan

University of Illinois at Urbana-Champaign

H-index: 4

North America-United States

About Aamir Hasan

Aamir Hasan, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at University of Illinois at Urbana-Champaign, specializes in the field of Robotics, Human-Robot Interaction, Autonomous Vehicles, Machine Perception.

His recent articles reflect a diverse array of research interests and contributions to the field:

Beyond the Dashboard: Investigating Distracted Driver Communication Preferences for ADAS

Dragon: A dialogue-based robot for assistive navigation with visual language grounding

Designing a Wayfinding Robot for People with Visual Impairments

Structural attention-based recurrent variational autoencoder for highway vehicle anomaly detection

PeRP: Personalized residual policies for congestion mitigation through co-operative advisory systems

Towards co-operative congestion mitigation

CoCAtt: A cognitive-conditioned driver attention dataset

An interdisciplinary approach: Potential for robotic support to address wayfinding barriers among persons with visual impairments

Aamir Hasan Information

University

University of Illinois at Urbana-Champaign

Position

Graduate Student

Citations(all)

56

Citations(since 2020)

56

Cited By

3

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

1

i10Index(since 2020)

1

Email

University Profile Page

University of Illinois at Urbana-Champaign

Aamir Hasan Skills & Research Interests

Robotics

Human-Robot Interaction

Autonomous Vehicles

Machine Perception

Top articles of Aamir Hasan

Beyond the Dashboard: Investigating Distracted Driver Communication Preferences for ADAS

Authors

Aamir Hasan,D Livingston McPherson,Melissa Miles,Katherine Driggs-Campbell

Journal

arXiv preprint arXiv:2403.03312

Published Date

2024/3/5

Distracted driving is a major cause of road fatalities. With improvements in driver (in)attention detection, these distracted situations can be caught early to alert drivers and improve road safety and comfort. However, drivers may have differing preferences for the modes of such communication based on the driving scenario and their current distraction state. To this end, we present a user study (N=147) where videos of simulated driving scenarios were utilized to learn drivers preferences for modes of communication and their evolution with the drivers changing attention. The survey queried participants preferred modes of communication for scenarios such as collisions or stagnation at a green light. We validate our hypotheses and provide key results that inform the future of communication between drivers and their vehicles. We showcase the different driver preferences based on the nature of the driving scenario and also show that they evolve as the drivers distraction state changes.

Dragon: A dialogue-based robot for assistive navigation with visual language grounding

Authors

Shuijing Liu,Aamir Hasan,Kaiwen Hong,Runxuan Wang,Peixin Chang,Zachary Mizrachi,Justin Lin,D Livingston McPherson,Wendy A Rogers,Katherine Driggs-Campbell

Journal

IEEE Robotics and Automation Letters

Published Date

2024/2/6

Persons with visual impairments (PwVI) have difficulties understanding and navigating spaces around them. Current wayfinding technologies either focus solely on navigation or provide limited communication about the environment. Motivated by recent advances in visual-language grounding and semantic navigation, we propose DRAGON, a guiding robot powered by a dialogue system and the ability to associate the environment with natural language. By understanding the commands from the user, DRAGON is able to guide the user to the desired landmarks on the map, describe the environment, and answer questions from visual observations. Through effective utilization of dialogue, the robot can ground the user's free-form language to the environment, and give the user semantic information through spoken language. We conduct a user study with blindfolded participants in an everyday indoor environment …

Designing a Wayfinding Robot for People with Visual Impairments

Authors

Shuijing Liu,Aamir Hasan,Kaiwen Hong,Chun-Kai Yao,Justin Lin,Weihang Liang,Megan A Bayles,Wendy A Rogers,Katherine Driggs-Campbell

Journal

arXiv preprint arXiv:2302.09144

Published Date

2023/2/17

People with visual impairments (PwVI) often have difficulties navigating through unfamiliar indoor environments. However, current wayfinding tools are fairly limited. In this short paper, we present our in-progress work on a wayfinding robot for PwVI. The robot takes an audio command from the user that specifies the intended destination. Then, the robot autonomously plans a path to navigate to the goal. We use sensors to estimate the real-time position of the user, which is fed to the planner to improve the safety and comfort of the user. In addition, the robot describes the surroundings to the user periodically to prevent disorientation and potential accidents. We demonstrate the feasibility of our design in a public indoor environment. Finally, we analyze the limitations of our current design, as well as our insights and future work. A demonstration video can be found at https://youtu.be/BS9r5bkIass.

Structural attention-based recurrent variational autoencoder for highway vehicle anomaly detection

Authors

Neeloy Chakraborty,Aamir Hasan,Shuijing Liu,Tianchen Ji,Weihang Liang,D Livingston McPherson,Katherine Driggs-Campbell

Published Date

2023/1/9

In autonomous driving, detection of abnormal driving behaviors is essential to ensure the safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that modeling interactions between agents improves detection accuracy, but certain abnormal behaviors where structured road information is paramount are poorly identified, such as wrong-way and off-road driving. We propose a novel unsupervised framework for highway anomaly detection named Structural Attention-Based Recurrent VAE (SABeR-VAE), which explicitly uses the structure of the environment to aid anomaly identification. Specifically, we use a vehicle self-attention module to learn the relations among vehicles on a road, and a separate lane-vehicle attention module to model the importance of permissible lanes to aid in trajectory prediction. Conditioned on the attention modules' outputs, a recurrent encoder-decoder architecture with a stochastic Koopman operator-propagated latent space predicts the next states of vehicles. Our model is trained end-to-end to minimize prediction loss on normal vehicle behaviors, and is deployed to detect anomalies in (ab)normal scenarios. By combining the heterogeneous vehicle and lane information, SABeR-VAE and its deterministic variant, SABeR-AE, improve abnormal AUPR by 18% and 25% respectively on the simulated MAAD highway dataset over STGAE-KDE. Furthermore, we show that the learned Koopman operator in SABeR-VAE enforces interpretable structure in the variational latent space. The results of our method indeed show that modeling environmental factors is essential to detecting a diverse set of …

PeRP: Personalized residual policies for congestion mitigation through co-operative advisory systems

Authors

Aamir Hasan,Neeloy Chakraborty,Haonan Chen,Jung-Hoon Cho,Cathy Wu,Katherine Driggs-Campbell

Published Date

2023/9/24

Intelligent driving systems can be used to mitigate congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these systems assume precise control over autonomous vehicle fleets, and are hence limited in practice as they fail to account for uncertainty in human behavior. Piecewise Constant (PC) Policies address these issues by structurally modeling the likeness of human driving to reduce traffic congestion in dense scenarios to provide action advice to be followed by human drivers. However, PC policies assume that all drivers behave similarly. To this end, we develop a co-operative advisory system based on PC policies with a novel driver trait conditioned Personalized Residual Policy, PeRP. PeRP advises drivers to behave in ways that mitigate traffic congestion. We first infer the driver's intrinsic traits on how they follow instructions in an …

Towards co-operative congestion mitigation

Authors

Aamir Hasan,Neeloy Chakraborty,Cathy Wu,Katherine Driggs-Campbell

Journal

arXiv preprint arXiv:2302.09140

Published Date

2023/2/17

The effects of traffic congestion are widespread and are an impedance to everyday life. Piecewise constant driving policies have shown promise in helping mitigate traffic congestion in simulation environments. However, no works currently test these policies in situations involving real human users. Thus, we propose to evaluate these policies through the use of a shared control framework in a collaborative experiment with the human driver and the driving policy aiming to co-operatively mitigate congestion. We intend to use the CARLA simulator alongside the Flow framework to conduct user studies to evaluate the affect of piecewise constant driving policies. As such, we present our in-progress work in building our framework and discuss our proposed plan on evaluating this framework through a human-in-the-loop simulation user study.

CoCAtt: A cognitive-conditioned driver attention dataset

Authors

Yuan Shen,Niviru Wijayaratne,Pranav Sriram,Aamir Hasan,Peter Du,Katherine Driggs-Campbell

Published Date

2022/10/8

The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preventing high-risk events, like collisions and casualties. However, existing driver attention prediction models neglect the distraction state and intention of the driver, which can significantly influence how they observe their surroundings. To address these issues, we present a new driver attention dataset, CoCAtt (Cognitive-Conditioned Attention). Unlike previous driver attention datasets, CoCAtt includes perframe annotations that describe the distraction state and intention of the driver. In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions. Our results demonstrate that incorporating the above two driver states into …

An interdisciplinary approach: Potential for robotic support to address wayfinding barriers among persons with visual impairments

Authors

Megan A Bayles,Travis Kadylak,Shuijing Liu,Aamir Hasan,Weihang Liang,Kaiwen Hong,Kathrine Driggs-Campbell,Wendy A Rogers

Journal

Proceedings of the Human Factors and Ergonomics Society Annual Meeting

Published Date

2022/9

Persons with Vision Impairments (PwVI) often have difficulties navigating indoor environments. The challenges and solutions can change based on their level of familiarity with the location. A collaborative effort was made to design a user needs assessment to understand the collaborative nature of human-robot interaction for wayfinding. The user study was an interview study to discuss with PwVI their navigation experience in familiar, somewhat familiar, and unfamiliar locations. Following this, we discussed their current solution strategies for wayfinding in those locations to discuss how they could imagine a robot to support wayfinding. We report on four case studies to illustrate specific user needs, such as vocal direction and orientation to learn a new environment and navigate, and highlight common strategies, such as supplemental lighting, different types of human assistance, and technologies used (i.e. white …

Meta-path analysis on spatio-temporal graphs for pedestrian trajectory prediction

Authors

Aamir Hasan,Pranav Sriram,Katherine Driggs-Campbell

Published Date

2022/5/23

Spatio-temporal graphs (ST-graphs) have been used to model time series tasks such as traffic forecasting, human motion modeling, and action recognition. The high-level structure and corresponding features from ST-graphs have led to improved performance over traditional architectures. However, current methods tend to be limited by simple features, despite the rich information provided by the full graph structure, which leads to inefficiencies and suboptimal performance in downstream tasks. We propose the use of features derived from meta-paths, walks across different types of edges, in ST-graphs to improve the performance of Structural Recurrent Neural Network. In this paper, we present the Meta-path Enhanced Structural Recurrent Neural Network (MESRNN), a generic framework that can be applied to any spatio-temporal task in a simple and scalable manner. We employ MESRNN for pedestrian trajectory …

Long-term pedestrian trajectory prediction using mutable intention filter and warp LSTM

Authors

Zhe Huang,Aamir Hasan,Kazuki Shin,Ruohua Li,Katherine Driggs-Campbell

Journal

IEEE Robotics and Automation Letters

Published Date

2020/12/28

Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians. Critical insights from human intention and behavioral patterns need to be integrated to effectively forecast long-term pedestrian behavior. Thus, we propose a framework incorporating a mutable intention filter and a Warp LSTM (MIF-WLSTM) to simultaneously estimate human intention and perform trajectory prediction. The mutable intention filter is inspired by particle filtering and genetic algorithms, where particles represent intention hypotheses that can be mutated throughout the pedestrian’s motion. Instead of predicting sequential displacement over time, our Warp LSTM learns to generate offsets on a full trajectory predicted by a nominal intention-aware linear model, which considers the intention hypotheses during filtering process. Through experiments on a publicly available dataset, we show that our …

Intention-aware residual bidirectional lstm for long-term pedestrian trajectory prediction

Authors

Zhe Huang,Aamir Hasan,Katherine Driggs-Campbell

Journal

Journal of Environmental Sciences (China) English Ed

Published Date

2020/6/30

Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians. Critical insights from human intention and behavioral patterns need to be effectively integrated into long-term pedestrian behavior forecasting. We present a novel intention-aware motion prediction framework, which consists of a Residual Bidirectional LSTM (ReBiL) and a mutable intention filter. Instead of learning step-wise displacement, we propose learning offset to warp a nominal intention-aware linear prediction, giving residual learning a physical intuition. Our intention filter is inspired by genetic algorithms and particle filtering, where particles mutate intention hypotheses throughout the pedestrian’s motion with ReBiL as the motion model. Through experiments on a publicly available dataset, we show that our method outperforms baseline approaches and the robust performance of our method is demonstrated under abnormal intention-changing scenarios.

See List of Professors in Aamir Hasan University(University of Illinois at Urbana-Champaign)

Aamir Hasan FAQs

What is Aamir Hasan's h-index at University of Illinois at Urbana-Champaign?

The h-index of Aamir Hasan has been 4 since 2020 and 4 in total.

What are Aamir Hasan's top articles?

The articles with the titles of

Beyond the Dashboard: Investigating Distracted Driver Communication Preferences for ADAS

Dragon: A dialogue-based robot for assistive navigation with visual language grounding

Designing a Wayfinding Robot for People with Visual Impairments

Structural attention-based recurrent variational autoencoder for highway vehicle anomaly detection

PeRP: Personalized residual policies for congestion mitigation through co-operative advisory systems

Towards co-operative congestion mitigation

CoCAtt: A cognitive-conditioned driver attention dataset

An interdisciplinary approach: Potential for robotic support to address wayfinding barriers among persons with visual impairments

...

are the top articles of Aamir Hasan at University of Illinois at Urbana-Champaign.

What are Aamir Hasan's research interests?

The research interests of Aamir Hasan are: Robotics, Human-Robot Interaction, Autonomous Vehicles, Machine Perception

What is Aamir Hasan's total number of citations?

Aamir Hasan has 56 citations in total.

What are the co-authors of Aamir Hasan?

The co-authors of Aamir Hasan are Wendy A. Rogers, Katherine Driggs-Campbell, Cathy Wu, Travis Kadylak, D. Livingston McPherson, Shuijing Liu.

    Co-Authors

    H-index: 75
    Wendy A. Rogers

    Wendy A. Rogers

    University of Illinois at Urbana-Champaign

    H-index: 24
    Katherine Driggs-Campbell

    Katherine Driggs-Campbell

    University of Illinois at Urbana-Champaign

    H-index: 16
    Cathy Wu

    Cathy Wu

    University of California, Berkeley

    H-index: 13
    Travis Kadylak

    Travis Kadylak

    University of Illinois at Urbana-Champaign

    H-index: 6
    D. Livingston McPherson

    D. Livingston McPherson

    University of California, Berkeley

    H-index: 6
    Shuijing Liu

    Shuijing Liu

    University of Illinois at Urbana-Champaign

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