Saeed Saadatnejad

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Publications associées (18)

Vehicle trajectory prediction works, but not everywhere

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

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, Saeed Saadatnejad, Taylor Ferdinand Mordan, Yuejiang Liu, Behnam Parsaeifard

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, Taylor Ferdinand Mordan, Siyuan Li

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

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