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Revolutionizing Navigation: How Open-Source Maps Enhance Driverless Cars

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Chapter 1: The Need for Enhanced Mapping

The effectiveness of current street maps in aiding autonomous vehicles is questionable. While we’ve charted most roadways, these maps are seldom updated, typically only once every few years. Additionally, they often lack critical roadside details like signs, lane markings, and driveways. This missing layer of information complicates the safe navigation of self-driving cars through urban environments, as well as robotic delivery systems that depend on accurate data regarding road surfaces and obstacles.

Street-level imagery for autonomous navigation

To address this issue, the Swedish startup Mapillary is stepping in with a solution. This innovative platform collects images of streets contributed by individuals using their smartphones, forming a kind of community-driven mapping resource—akin to a Wikipedia for streets. As a result, it has become one of the largest publicly accessible databases of street-level imagery globally.

“Self-driving vehicles demand the most current view of road conditions,” says CEO Jan Erik Solem. “The frequency of map updates needs to increase dramatically—from quarterly to daily. Leveraging technology is the only scalable solution.”

Section 1.1: The Competitive Landscape

Various methods exist for gathering mapping data, and competition among startups is fierce. Mapillary distinguishes itself through its crowdsourced model, in contrast to proprietary systems like Google’s Street View, which only refreshes its data every few years. The platform benefits from daily updates, as anyone can contribute new images.

Subsection 1.1.1: The Power of Community

Crowdsourced mapping data

Mapillary’s approach mirrors that of OpenStreetMap, which has provided a free and editable world map since 2004, but it offers additional roadside details. The company employs computer vision technology to analyze user-uploaded images and identify various objects. Their expansive database includes 422 million images, spanning 6.2 million kilometers (3.9 million miles) of roads, continually expanding as new data comes in.

Section 1.2: Practical Applications of Mapping

Once images are uploaded, they are processed to identify objects, which are then mapped and made publicly accessible online. While the service is free for charitable and educational purposes, commercial users must pay a licensing fee.

Mapillary is already being utilized by various municipalities. Cities like Amsterdam and transport officials in Vermont and Arizona, as well as the nation of Lithuania, are leveraging this data to enhance their street mapping efforts.

Chapter 2: Automation in Urban Management

This video discusses the advantages of driverless cars and how they could be the safest mode of transportation, emphasizing the importance of accurate and up-to-date maps.

Mapillary is also aiding the city of Clovis in New Mexico, ensuring that they meet community standards for maintaining clear driveways, up-to-date signs, and pothole-free roads. Previously, the city would hire contractors to manually catalog every mailbox and fire hydrant, a labor-intensive process.

Automated street management through Mapillary

By employing Mapillary, Clovis has automated much of this tedious work. “Without this software, we would have to gather all the data ourselves, and estimating the time required for a 23-square-mile city would be nearly impossible,” notes Steven Hewett from Clovis.

Gabriel Brostow, a computer science professor at University College London, asserts that automating this mapping process is crucial for scalability. “Humans cannot update millions of square kilometers as rapidly as algorithms can,” he explains.

In this video, we explore the challenges faced by pedestrians in urban environments, as highlighted by OpenStreetMap’s work, underscoring the need for comprehensive mapping.

Looking ahead, there is potential for driverless vehicles to not only consume but also generate mapping data by capturing street-level imagery as they navigate. This would include automatic blurring of faces and license plates to protect privacy. Hewett envisions a future where connected vehicles can recognize and report issues like fallen trees or traffic accidents directly to local authorities.

Furthermore, this data can empower cities with deeper insights into their infrastructures, enabling improvements in public transport and accessibility for individuals with disabilities, as Brostow points out.

Charlotte Jee, the author, writes "The Download," a daily tech newsletter. Before joining MIT Technology Review, she was the editor of Techworld and reported on the intersection of politics and technology. In her free time, she runs Jeneo, a venture focused on inclusivity in tech events, and frequently appears on the BBC.

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