Taylor Ferdinand Mordan

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The ability to predict pedestrian behaviour is crucial for road safety, traffic management systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous vehicles. We present a vision-based system that simultaneously locates where pedestri ...

Predicting 3D human poses in real-world scenarios, also known as human pose forecasting, is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and occlusions. To address these challenges, we propose a diffusion-based approach th ...

Monocular and stereo visions are cost-effective solutions for 3D human localization in the context of self-driving cars or social robots. However, they are usually developed independently and have their respective strengths and limitations. We propose a no ...

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, s ...

Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to tackle distributional shifts. Despite encouraging results, it remains unclear when this approach thrives or fails. In this work, we first provide an in-depth look at ...

A powerful simulator highly decreases the need for real-world tests when training and evaluating autonomous vehicles. Data-driven simulators flourished with the recent advancement of conditional Generative Adversarial Networks (cGANs), providing high-fidel ...

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 ...

Human pose forecasting involves complex spatiotemporal interactions between body parts (e.g., arms, legs, spine). State-of-the-art approaches use Long Short-Term Memories (LSTMs) or Variational AutoEncoders (VAEs) to solve the problem. Yet, they do not eff ...

Over the past few years, deep Convolutional Neural Networks have shown outstanding performance on semantic segmentation, which is an essential tool needed by self-driving cars to understand their environments. However, their training relies on large datase ...

Self-driving cars and delivery robots are set to shape the future of transportation, but they still have to learn how to co-exist with humans in close proximity. Autonomous systems need to detect pedestrians and understand the meaning of their actions befo ...

Taylor Ferdinand Mordan

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Pedestrian intention prediction: A convolutional bottom-up multi-task approach

A generic diffusion-based approach for 3D human pose prediction in the wild

MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

A Shared Representation for Photorealistic Driving Simulators

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Learning Decoupled Representations for Human Pose Forecasting

Bilinear Multimodal Discriminator for Adversarial Domain Adaptation with Privileged Information

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Simple Yet Effective Action Recognition for Autonomous Driving