Publications by Saeed Saadatnejad

Certified Human Trajectory Prediction

Alexandre Massoud Alahi, Seyed Mohsen Moosavi Dezfooli, Saeed Saadatnejad, Mohammadhossein Bahari

Predicting human trajectories is essential for the safe operation of autonomous vehicles, yet current data-driven models often lack robustness in case of noisy inputs such as adversarial examples or imperfect observations. Although some trajectory predicti ...

2025

HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario

Alexandre Massoud Alahi, Saeed Saadatnejad

Institute of Electrical and Electronics Engineers (IEEE)2025

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

IEEE Institute of Electrical and Electronics Engineers2025

MotionMap: Representing Multimodality in Human Pose Forecasting

Mathieu Salzmann, Alexandre Massoud Alahi, Saeed Saadatnejad, Megh Hiren Shukla

Human pose forecasting is inherently multimodal since multiple futures exist for an observed pose sequence. However, evaluating multimodality is challenging since the task is illposed. Therefore, we first propose an alternative paradigm to make the task we ...

2025

JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao

Predicting future trajectories is critical in autonomous driving, especially in preventing accidents involving humans, where a predictive agent’s ability to anticipate in advance is of utmost importance. Trajectory forecasting models, employed in fields su ...

2024

Toward Reliable Human Pose Forecasting With Uncertainty

Alexandre Massoud Alahi, Saeed Saadatnejad, Taylor Ferdinand Mordan, Parham Saremi

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 ...

2024

Social-Transmotion: Promptable Human Trajectory Prediction

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao, Kaouther Messaoud Ben Amor

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 ...

2024

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

Alexandre Massoud Alahi, Saeed Saadatnejad, Taylor Ferdinand Mordan

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 ...

IEEE2023

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Saeed Saadatnejad

Forecasting is a capability inherent in humans when navigating. Humans routinely plan their paths, considering the potential future movements of those around them. Similarly, to achieve comparable sophistication and safety, autonomous systems must embrace ...

EPFL2023

Are socially-aware trajectory prediction models really socially-aware?

Alexandre Massoud Alahi, Seyed Mohsen Moosavi Dezfooli, Saeed Saadatnejad, Mohammadhossein Bahari, Pedram Khorsandi

Our transportation field has recently witnessed an arms race of neural network-based trajectory predictors. While these predictors are at the core of many applications such as autonomous navigation or pedestrian flow simulations, their adversarial robustne ...

2022

Vehicle trajectory prediction works, but not everywhere

Alexandre Massoud Alahi, Seyed Mohsen Moosavi Dezfooli, Saeed Saadatnejad, Mohammadhossein Bahari, Ahmad Rahimi

Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the industry and research communities have acknowledged the need for such a pillar by providing public benchmarks. While state-of-the-art methods are impressive, i.e. ...

IEEE2022

Pedestrian 3D Bounding Box Prediction

Alexandre Massoud Alahi, Saeed Saadatnejad, Yi Zhou Ju

Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained pre ...

2022

Learning Decoupled Representations for Human Pose Forecasting

Alexandre Massoud Alahi, Yuejiang Liu, Behnam Parsaeifard, Saeed Saadatnejad, Taylor Ferdinand Mordan

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 ...

IEEE2021

Modified Joint Channel-and-Data Estimation for One-Bit Massive MIMO

Saeed Saadatnejad, Mohammadhossein Bahari, Mahdi Amiri

Centralized and cloud computing-based network architectures are the promising tracks of future communication systems where a large scale compute power can be virtualized for various algorithms. These architectures rely on high-performance communication lin ...

IEEE2021

A Shared Representation for Photorealistic Driving Simulators

Alexandre Massoud Alahi, Saeed Saadatnejad, Siyuan Li, Taylor Ferdinand Mordan

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 ...

IEEE Institute of Electrical and Electronics Engineers2021

Next Steps for Image Synthesis using Semantic Segmentation

Alexandre Massoud Alahi, Saeed Saadatnejad

Image synthesis in the desired semantic can be used in many tasks of self-driving cars giving us the possibility to enhance existing challenging datasets by realistic-looking images which we do not have enough. Our goal is to improve the image quality gene ...

2020

Pedestrian Intention Prediction: A Multi-task Perspective

Alexandre Massoud Alahi, Saeed Saadatnejad

In order to be globally deployed, autonomous cars must guarantee the safety of pedestrians. This is the reason why forecasting pedestrians' intentions sufficiently in advance is one of the most critical and challenging tasks for autonomous vehicles. This w ...

2020

Pedestrian Image Generation for Self-driving Cars

Alexandre Massoud Alahi, Saeed Saadatnejad

Pedestrian image generation in the desired pose can be used in a wide range of applications e.g., person re-identification and tracking which are among the fundamental challenges in self-driving cars. This is a hard task because it should be invariant to a ...

2019