Publication

Reshaping Perception for Autonomous Driving with Semantic Keypoints

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Unsupervised Stereo Matching Using Confidential Correspondence Consistency

Seungryong Kim

Stereo matching aims to perceive the 3D geometric configuration of scenes and facilitates a variety of computer vision in advanced driver assistance systems (ADAS) applications. Recently, deep convolutional neural networks (CNNs) have shown dramatic perfor ...

2020

STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

Jie Luo, Yiyu Wang, Mengshi Qi

Predictive scene parsing is a task of assigning pixel-level semantic labels to a future frame of a video. It has many applications in vision-based artificial intelligent systems, e.g., autonomous driving and robot navigation. Although previous work has sho ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

, , ,

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

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

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

Alexandre Massoud Alahi, Sven Kreiss, Taylor Ferdinand Mordan, Lorenzo Bertoni

Monocular and stereo vision 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 nov ...

2020

Materializing Personal Standing Mobility from Design to Shared Control

Aude Billard, Diego Felipe Paez Granados

We introduce a unique intelligent system that relies on the centre of mass motions for conveying the user's motion intention. The objective of this device is to achieve a standing mobility vehicle for users with lower body impairments, such as spinal cord ...

2020

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

Object Classification Based on Unsupervised Learned Multi-Modal Features For Overcoming Sensor Failures

Roland Siegwart

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

SegMap: Segment-based mapping and localization using data-driven descriptors

Roland Siegwart, Andrei Cramariuc

Precisely estimating a robot’s pose in a prior, global map is a fundamental capability for mobile robotics, e.g., autonomous driving or exploration in disaster zones. This task, however, remains challenging in unstructured, dynamic environments, where loca ...

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