PASS: Panoramic Annular Semantic Segmentation

Most autonomous car control frameworks are based on a middleware layer with several independent modules that are connected by an inter-process communication mechanism. These modules implement basic actions and report events about their state by subscribing and publishing messages. Here, we propose an executive module that coordinates the activity of these modules. This executive module uses hierarchical interpreted binary Petri nets (PNs) to define the behavior expected from the car in different scenarios according to the traffic rules. The module commands actions by sending messages to other modules and evolves its internal state according to the events (messages) received. A programming environment named RoboGraph (RG) is introduced with this architecture. RG includes a graphical interface that allows the edition, execution, tracing, and maintenance of the PNs. For the execution, a dispatcher loads these PNs and executes the different behaviors. The RG monitor that shows the state of all the running nets has proven to be very useful for debugging and tracing purposes. The whole system has been applied to an autonomous car designed for elderly or disabled people.

Section: Resultsmentioning

confidence: 99%

Implementing Autonomous Driving Behaviors Using a Message Driven Petri Net Framework

López

Sánchez-Vilariño

Sanz

et al. 2020

“…SwaftNet is trained on Mapillary Vistas [ 3 ], which is a street scene dataset that includes many images captured by pedestrians on sidewalks. In addition, we use a heterogeneous set of data augmentation techniques that are of critical relevance to the generalization capacity in unseen domains [ 51 ]. Thereby, the semantic segmentation module performs robustly with glasses for blind people.…”

Section: Systemmentioning

confidence: 99%

“…Mapillary Vistas contains views from multiple cities around the world, but those images belong to the very specific street view scenario and lack challenging images from scenarios in which we aim to apply our method; e.g., egomotion indoors and outdoors. To assess the real-world semantic segmentation accuracy of our trained SwaftNet and determine its performance in scenarios in which it was not trained, we evaluated SwaftNet on the PASS dataset [ 51 ], which was captured by a wearable navigation assistance system. The PASS dataset better reflects the targeted scenarios, as it was captured using head-mounted lenses; thus, it was an ideal dataset to estimate the real-world semantic segmentation of our system.…”

Section: Technical Evaluationmentioning

confidence: 99%

Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video

Martínez

Yang

Constantinescu

et al. 2020

Self Cite

The current COVID-19 pandemic is having a major impact on our daily lives. Social distancing is one of the measures that has been implemented with the aim of slowing the spread of the disease, but it is difficult for blind people to comply with this. In this paper, we present a system that helps blind people to maintain physical distance to other persons using a combination of RGB and depth cameras. We use a real-time semantic segmentation algorithm on the RGB camera to detect where persons are and use the depth camera to assess the distance to them; then, we provide audio feedback through bone-conducting headphones if a person is closer than 1.5 m. Our system warns the user only if persons are nearby but does not react to non-person objects such as walls, trees or doors; thus, it is not intrusive, and it is possible to use it in combination with other assistive devices. We have tested our prototype system on one blind and four blindfolded persons, and found that the system is precise, easy to use, and amounts to low cognitive load.

“…A segmentation method with artificial intelligence such as deep learning is one of good solution to detect the RVA [ 30 ]. However, deep learning requires a huge amount of data based on the type of vehicle, camera parameters, and various driving environments.…”

Section: Introductionmentioning

confidence: 99%

A Hough-Space-Based Automatic Online Calibration Method for a Side-Rear-View Monitoring System

Lee

2020

We propose an automatic camera calibration method for a side-rear-view monitoring system in natural driving environments. The proposed method assumes that the camera is always located near the surface of the vehicle so that it always shoots a part of the vehicle. This method utilizes photographed vehicle information because the captured vehicle always appears stationary in the image, regardless of the surrounding environment. The proposed algorithm detects the vehicle from the image and computes the similarity score between the detected vehicle and the previously stored vehicle model. Conventional online calibration methods use additional equipment or operate only in specific driving environments. On the contrary, the proposed method is advantageous because it can automatically calibrate camera-based monitoring systems in any driving environment without using additional equipment. The calibration range of the automatic calibration method was verified through simulations and evaluated both quantitatively and qualitatively through actual driving experiments.