Alexandre Massoud Alahi

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Autonomous vehicles ought to predict the surrounding agents' trajectories to allow safe maneuvers in uncertain and complex traffic situations. As companies increasingly apply trajectory prediction in the real world, security becomes a relevant concern. In this paper, we focus on backdoors - a security threat acknowledged in other fields but so far overlooked for trajectory prediction. To this end, we describe and investigate four triggers that could affect trajectory prediction. We then show that these trig

Recent scientific and technological advancements have transformed the knowledge frontiers, giving rise to the next wave of disruptive technologies with deep impacts on urban society. An Urban Digital Twin (UDT) is a technology with great potential to trans ...

We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images. Driven by the limitation of neural networks outputting point estimates, we address the ambiguity in the task by predicting confidence intervals through a loss ...

Transportation is the lifeblood of society, connecting people, goods, places, and opportunities. It ignites the spark that fuels progress, personal joy, and human discovery. It is intrinsically tied to the concept of mobility, that is, the ability to move or be moved. One can see transportation as something you do and mobility as something you have. Mobility is something everyone should have—a universal right that should be collision-free and stress-free. How can we contribute to a society where anyone can

Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and security risks of AI. For example, while models are often studied in isolation, th ...

Recently, there has been an arms race of pose forecasting methods aimed at solving the spatio-temporal task of predicting a sequence of future 3D poses of a person given a sequence of past observed ones. However, the lack of unified benchmarks and limited ...

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

We propose TAROT, a Targeted data selection framework grounded in Optimal Transport theory. Previous targeted data selection methods primarily use influencebased greedy heuristics to enhance domain-specific performance. These methods perform well on limite ...

Purpose: In this work, we present a new machine learning method based on the transformer neural network to detect eye rubbing using a smartwatch in a real-life setting. In ophthalmology, the accurate detection and prevention of eye rubbing could reduce inc ...

Autonomous off-road navigation faces challenges due to diverse, unstructured environments, requiring robust perception with both geometric and semantic understanding. However, scarce densely labeled semantic data limits generalization across domains. Simulated data helps, but introduces domain adaptation issues. We propose COARSE, a semi-supervised domain adaptation framework for off-road semantic segmentation, leveraging sparse, coarse in-domain labels and densely labeled out-of-domain data. Using pretrain

Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting the future trajectory of people based on their past positions. Following the recent success of Recurrent Neural Network (RNN) models

Motion forecasting is crucial in enabling autonomous vehicles to anticipate the future trajectories of surrounding agents. To do so, it requires solving mapping, detection, tracking, and then forecasting problems, in a multi-step pipeline. In this complex ...

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. These encompass visual ...

Transportation systems often rely on understanding the flow of vehicles or pedestrian. From traffic monitoring at the city scale, to commuters in train terminals, recent progress in sensing technology make it possible to use cameras to better understand th ...

As AI usage becomes more ubiquitous, AI incident reporting is both practiced increasingly in industry and mandated by regulatory requirements. At the same time, it is established that AI systems are exploited in practice by a growing number of security threats. Yet, organizations and practitioners lack necessary guidance in describing AI security incidents. In this position paper, we argue that specific taxonomies are required to describe and report security incidents of AI systems. In other words, existing

BACKGROUND: Modern health concerns related to air pollutant exposure in buildings have been exacerbated owing to several factors. Methods for assessing inhalation exposures indoors have been restricted to stationary air pollution measurements, typically as ...

Transportation systems often rely on understanding the flow of vehicles or pedestrian. Fromtraffic monitoring at the city scale, to commuters in train terminals, recent progress in sensingtechnology make it possible to use cameras to better understand the ...

The ability of intelligent systems to predict human behaviors is crucial, particularly in fields such as autonomous vehicle navigation and social robotics. However, the complexity of human motion have prevented the development of a standardized dataset for human motion prediction, thereby hindering the establishment of pre-trained models. In this paper, we address these limitations by integrating multiple datasets, encompassing both trajectory and 3D pose keypoints, to propose a pre-trained model for human

Deep motion forecasting models have achieved great success when trained on a massive amount of data. Yet, they often perform poorly when training data is limited. To address this challenge, we propose a transfer learning approach for efficiently adapting p ...

Autonomous vehicles rely on an accurate perception module. One of the fundamental challenges is to efficiently track pedestrians surrounding a vehicle to anticipate risky situations. Over the past decades, researchers have formulated the tracking problem a ...

Alexandre Massoud Alahi

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Manipulating Trajectory Prediction with Backdoors

Urban Digital Twin Challenges: A Systematic Review and Perspectives for Sustainable Smart Cities

MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

Foundational principles for representation learning: applications in autonomous mobility

Towards more Practical Threat Models in Artificial Intelligence Security

Toward Reliable Human Pose Forecasting With Uncertainty

Social-Transmotion: Promptable Human Trajectory Prediction

TAROT: Targeted Data Selection via Optimal Transport

Eye-Rubbing Detection Using a Smartwatch: A Feasibility Study Demonstrated High Accuracy With Machine Learning

COARSE: Collaborative Pseudo-Labeling with Coarse Real Labels for Off-Road Semantic Segmentation

Learning to Predict Human Behavior in Crowded Scenes

Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Deep Visual Re-Identification with Confidence

Position: A taxonomy for reporting and describing AI security incidents

Proxy methods for detection of inhalation exposure in simulated office environments

Deep visual Re-identification with confidence

Multi-Transmotion: Pre-trained Model for Human Motion Prediction

Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting

Learning nuisances to track pedestrians in autonomous vehicles