82 citations · 305 across the 22 of their papers we have counts for
23 papers
Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment
Amir Rasouli, Iuliia Kotseruba
In this paper, we delve into the pedestrian behavior understanding problem from the perspective of three different tasks: intention estimation, action prediction, and event risk as…
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving
Mozhgan Pourkeshavarz, Mohammad Sabokrou, Amir Rasouli
In autonomous driving, behavior prediction is fundamental for safe motion planning, hence the security and robustness of prediction models against adversarial attacks are of paramo…
Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making
Iuliia Kotseruba, Amir Rasouli
Current research on pedestrian behavior understanding focuses on the dynamics of pedestrians and makes strong assumptions about their perceptual abilities. For instance, it is ofte…
NeurIPS 2022 Competition: Driving SMARTS
Amir Rasouli, Randy Goebel, Matthew E. Taylor +15
Driving SMARTS is a regular competition designed to tackle problems caused by the distribution shift in dynamic interaction contexts that are prevalent in real-world autonomous dri…
PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning
Amir Rasouli, Iuliia Kotseruba
Predicting pedestrian behavior is a crucial task for intelligent driving systems. Accurate predictions require a deep understanding of various contextual elements that potentially…
Intend-Wait-Cross: Towards Modeling Realistic Pedestrian Crossing Behavior
Amir Rasouli, Iuliia Kotseruba
In this paper, we present a microscopic agent-based pedestrian behavior model Intend-Wait-Cross. The model is comprised of rules representing behaviors of pedestrians as a series o…
LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction
Elmira Amirloo, Amir Rasouli, Peter Lakner +2
Multi-agent trajectory prediction is a fundamental problem in autonomous driving. The key challenges in prediction are accurately anticipating the behavior of surrounding agents an…
Pedestrian Simulation: A Review
Amir Rasouli
This article focuses on different aspects of pedestrian (crowd) modeling and simulation. The review includes: various modeling criteria, such as granularity, techniques, and factor…
PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D
Amir Rasouli, Tiffany Yau, Peter Lakner +3
Predicting the behavior of road users, particularly pedestrians, is vital for safe motion planning in the context of autonomous driving systems. Traditionally, pedestrian behavior…
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction
Amir Rasouli, Mohsen Rohani, Jun Luo
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems. Pedestrians often exhibit complex behaviors influenced by various contextual elements…
Graph-SIM: A Graph-based Spatiotemporal Interaction Modelling for Pedestrian Action Prediction
Tiffany Yau, Saber Malekmohammadi, Amir Rasouli +3
One of the most crucial yet challenging tasks for autonomous vehicles in urban environments is predicting the future behaviour of nearby pedestrians, especially at points of crossi…
Multi-Modal Hybrid Architecture for Pedestrian Action Prediction
Amir Rasouli, Tiffany Yau, Mohsen Rohani +1
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems in urban environments. Pedestrians often exhibit a wide range of behaviors and adequat…
Deep Learning for Vision-based Prediction: A Survey
Amir Rasouli
Vision-based prediction algorithms have a wide range of applications including autonomous driving, surveillance, human-robot interaction, weather prediction. The objective of this…
Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs
Amir Rasouli, Iuliia Kotseruba, John K. Tsotsos
One of the major challenges for autonomous vehicles in urban environments is to understand and predict other road users' actions, in particular, pedestrians at the point of crossin…
Do Saliency Models Detect Odd-One-Out Targets? New Datasets and Evaluations
Iuliia Kotseruba, Calden Wloka, Amir Rasouli +1
Recent advances in the field of saliency have concentrated on fixation prediction, with benchmarks reaching saturation. However, there is an extensive body of works in psychology a…
Attention-based Active Visual Search for Mobile Robots
Amir Rasouli, Pablo Lanillos, Gordon Cheng +1
We present an active visual search model for finding objects in unknown environments. The proposed algorithm guides the robot towards the sought object using the relevant stimuli p…
Visual Attention and its Intimate Links to Spatial Cognition
John K. Tsotsos, Iuliia Kotseruba, Amir Rasouli +1
It is almost universal to regard attention as the facility that permits an agent, human or machine, to give priority processing resources to relevant stimuli while ignoring the irr…
Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice
Amir Rasouli, John K. Tsotsos
One of the major challenges that autonomous cars are facing today is driving in urban environments. To make it a reality, autonomous vehicles require the ability to communicate wit…
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice
Amir Rasouli, John K. Tsotsos
Today, one of the major challenges that autonomous vehicles are facing is the ability to drive in urban environments. Such a task requires communication between autonomous vehicles…
The Effect of Color Space Selection on Detectability and Discriminability of Colored Objects
Amir Rasouli, John K. Tsotsos
In this paper, we investigate the effect of color space selection on detectability and discriminability of colored objects under various conditions. 20 color spaces from the litera…
Integrating Three Mechanisms of Visual Attention for Active Visual Search
Amir Rasouli, John K. Tsotsos
Algorithms for robotic visual search can benefit from the use of visual attention methods in order to reduce computational costs. Here, we describe how three distinct mechanisms of…
Agreeing to Cross: How Drivers and Pedestrians Communicate
Amir Rasouli, Iuliia Kotseruba, John K. Tsotsos
The contribution of this paper is twofold. The first is a novel dataset for studying behaviors of traffic participants while crossing. Our dataset contains more than 650 samples of…
Joint Attention in Autonomous Driving (JAAD)
Iuliia Kotseruba, Amir Rasouli, John K. Tsotsos
In this paper we present a novel dataset for a critical aspect of autonomous driving, the joint attention that must occur between drivers and of pedestrians, cyclists or other driv…