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Despite pandemic-driven restrictions on movement, there were over 12,000 accidents in Madrid in 2020, leading to 31 fatalities. In Barcelona, there were more than 5,700 collisions, causing 14 deaths. Pedestrian and vehicle safety is a priority, which is why a research project at the Universitat Oberta de Catalunya (UOC) is harnessing artificial intelligence (AI) to make decisions that will make cities safer. The researchers have looked into the correlation between the complexity of certain urban areas and the likelihood of an accident occurring there.
According to the researchers, the data they have gathered can be used to train neural networks to detect probable hazards in an area and work out patterns associated with this high risk potential. The researchers, headed by Cristina Bustos and Javier Borge, are working with algorithms that will aid traffic authorities in reducing the likelihood of accidents in urban environments.
Hi HN!
We re Bert and Otto, founders of Segments.ai (https://segments.ai). Our platform helps computer vision teams build better datasets for image segmentation, an increasingly popular computer vision technique in the world of self-driving cars, autonomous robots, and AR/VR devices.
A large, curated dataset of labeled images is the first thing you need in any serious computer vision project. Building such datasets is a time-consuming endeavour, involving lots of manual labeling work. This is especially true for tasks like image segmentation, where every object and region in the image needs to be precisely annotated with a pixel-level segmentation mask. Manually segmenting a complex image can easily take up to an hour, even for experienced labelers. This leads to costs of tens to hundreds of thousands of dollars for labeling large datasets.
Feb 02, 2021
Time: 11:00 AM Eastern Standard Time
Duration: 1 hour
In February 2021, Ward’s Intelligence released new market research that underscores why it is critical for vehicle manufacturers to redefine operating strategies.
An extremely expanded ecosystem, new software-based architectures, broadly deployed connectivity and the ability to identify and monetize data are increasingly impacting automotive OEMs’ business models and profit pool. The development of a reliable and complete ecosystem is key for future success, both inside and outside the vehicle.
The new functionalities found in cars today and expected in the future are not about hardware; instead the industry is entering the “software-defined everything” era. Software-defined vehicles and “functionality as a service” will continue to drive new revenue streams in the future, as well as create cost-reduction opportunities along the entire automotive value chain.