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Stochastic Optimal Control for Autonomous Driving Applications via Polynomial Chaos Expansions

Publications associées (47)

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Safe Mission Planning under Dynamical Uncertainties

Maryam Kamgarpour

This paper considers safe robot mission planning in uncertain dynamical environments. This problem arises in applications such as surveillance, emergency rescue, and autonomous driving. It is a challenging problem due to mod-eling and integrating dynamical ...

IEEE2020

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

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 ...

IEEE2020

Driving is a very challenging task to automatize despite how naturally and efficiently it may come to experienced human drivers. The complexity stems from the need to (i) understand the surrounding context and forecast how it is likely to evolve, (ii) plan ...

EPFL2020

Improving sound absorption through nonlinear active electroacoustic resonators

Romain Christophe Rémy Fleury, Hervé Lissek, Xinxin Guo

Absorbing airborne noise at frequencies below 300 Hz is a particularly vexing problem due to the absence of natural sound absorbing materials at these frequencies. The prevailing solution for low-frequency sound absorption is the use of passive narrow-band ...

2020

Motion Prediction Using Temporal Inception Module

Pascal Fua, Mathieu Salzmann, Wei Wang, Sena Kiciroglu

Human motion prediction is a necessary component for many applications in robotics and autonomous driving. Recent methods propose using sequence-to-sequence deep learning models to tackle this problem. However, they do not focus on exploiting different tem ...

Springer, Cham2020

Manifold Learning and Optimal Control for Obstacle Avoidance in Autonomous Driving

Colin Neil Jones, Sanket Sanjay Diwale

A novel manifold learning approach is presented to incorporate computationally efficient obstacle avoidance constraints in optimal control algorithms. The method presented provides a significant computational benefit by reducing the number of constraints r ...

2019

Kernel methods and Model predictive approaches for Learning and Control

Sanket Sanjay Diwale

Data-driven modeling and feedback control play a vital role in several application areas ranging from robotics, control theory, manufacturing to management of assets, financial portfolios and supply chains. Many such problems in one way or another are rela ...

EPFL2019

Using Uncertainty Data in Chance-Constrained Trajectory Planning

Maryam Kamgarpour

We consider the problem of trajectory planning in an environment comprised of a set of obstacles with uncertain locations. While previous approaches model the uncertainties with a prescribed Gaussian distribution, we consider the realistic case in which th ...

IEEE2019

For autonomous driving applications it is critical to know which type of road users and road side infrastructure are present to plan driving manoeuvres accordingly. Therefore autonomous cars are equipped with different sensor modalities to robustly perceiv ...

2019

PifPaf: Composite Fields for Human Pose Estimation

Alexandre Massoud Alahi, Sven Kreiss, Lorenzo Bertoni

We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF) to localize body parts ...

IEEE2019