Data Mining Practical Machine Learning Tools and Techniques

The Visual Analysis of Human Movement

Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online.

nuScenes: A multimodal dataset for autonomous driving

Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side by side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

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nuScenes: A Multimodal Dataset for Autonomous Driving

Système de stéréovision pour la détection d'obstacles et de véhicule en temps réel

Pedestrian detection is a difficult task with significant practical interests especially in the field of Intelligent Transportation Systems. While the problem has been thoroughly studied from a visible, mid-wave infrared and far-wave infrared cameras perspective, little attention has been paid to the short-wave infrared spectrum. But although light in the short-wave infrared region is imperceptible to the eye, due to its reflective nature, it interacts with objects similarly to the visible wavelengths. As a result, short-wave images, including both shadows and contrast, are comparable to visible images in resolution and detail but they are not comparable in color, a feature which makes objects recognizable and yields one of the advantages of the short-waves, namely object or individual identification. In this paper we evaluate the pertinence of employing the short-wave bandwidths for the task of detecting pedestrians in clear visibility conditions.

/pdf/swir-images-evaluation-for-pedestrian-detection-in-clear-vnhd9k4sl4.pdf

SWIR images evaluation for pedestrian detection in clear visibility conditions

Intelligent Transportation Systems ConferenceIntelligent Vehicles Symposium

The RAS, “Robot Antartico di Superficie”, is intended to operate in a hostile and generally unknown environment, where the ability of sensing obstacles or special features (crevasses, sastrugi, composition of the soil) can represent the key point to successfully perform the required mission or to fail: wind storms (and many other atmospheric phenomena giving rise to white out) can make useless optical sensing equipment; ice microcrystals can penetrate practically everywhere, causing failures in many components; magnetic storms can blind GPS and radio equipment.

/pdf/the-sensing-subsystem-of-ras-17zsipx6xj.pdf

The Sensing Subsystem of RAS

Summary. This article presents a shape extraction and results of a preliminary validation stage for a pedestrian detection system based on the use of active contours. The complete system is based on the use of both far infrared and visible cameras to detect areas that potentially contain pedestrians; in order to validate and filter such result a refinement of the human shape by means of active contours is performed followed by a neural network based filtering.

/pdf/pedestrian-shape-extraction-by-means-of-active-contours-35sd9enolg.pdf

Alberto Broggi

Papers

Système de stéréovision pour la détection d'obstacles et de véhicule en temps réel

SWIR images evaluation for pedestrian detection in clear visibility conditions

Intelligent Transportation Systems ConferenceIntelligent Vehicles Symposium

The Sensing Subsystem of RAS

Pedestrian Shape Extraction by means of Active Contours