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The AI That Helps Humans See When Self-Driving Cars Go Wrong

MIT researchers have developed an AI system that helps humans understand why self-driving cars make certain decisions. The technology could help safety drivers respond to failures, engineers investigate crashes and regulators better understand autonomous-vehicle behaviour.

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Cars and a city bus travel through busy urban traffic, illustrating connected and autonomous vehicle technology.
Connected and autonomous vehicle technologies could help vehicles respond to hazards in complex urban traffic. Representational image. Image credit: Lee Starry/Pexels

Researchers from MIT and autonomous vehicles technology company Motional have developed a system that could help humans understand what is happening inside an autonomous vehicle when it makes a decision. Called the Concept-Wrapper Network, or CW-Net, it translates parts of the vehicle’s decision-making into concepts humans can understand, such as “approaching stopped vehicle” or “close to cyclist.”

In one test, an autonomous vehicle repeatedly stopped when approaching a cyclist. The safety driver assumed the vehicle had detected the cyclist. CW-Net revealed otherwise: the system was not properly configured to detect the cyclist. The vehicle had stopped because its emergency-braking procedure was triggered when it got too close. The difference is important. If a human knows that the vehicle has failed to recognise a cyclist, they can slow down or take control sooner.

It raises a larger question for autonomous driving: when a vehicle makes a mistake, can humans understand the mistake quickly enough to respond?

When the Decision Comes from Software

Autonomous vehicles use cameras, lidar and other sensors to interpret their surroundings. Machine-learning systems then use that information to plan the vehicle’s movement. But deep-learning models can be difficult to interpret. Engineers may see what the vehicle detected and what it eventually did without having a clear view of the reasoning connecting the two.

That problem has already appeared in crash investigations. In March 2018, an Uber test vehicle operating with an automated driving system struck and killed a pedestrian in Tempe, Arizona. The US National Transportation Safety Board found that the system had detected the pedestrian but classified her at different points as an unknown object, a vehicle and a bicycle. Its predictions about her path also changed.

The investigation showed why an autonomous-vehicle crash can require a different kind of analysis. Investigators may need to determine not just what happened, but what the automated system detected, how it classified an object and what it predicted before deciding how the vehicle should move.

What Happens After a Crash?

The US National Highway Traffic Safety Administration requires specified manufacturers and operators to report certain crashes involving automated driving systems and Level 2 driver-assistance systems. For covered automated-driving-system crashes, the reporting order includes cases in which the system was engaged within 30 seconds of a crash meeting specified injury, fatality, towing or property-damage criteria.

Such reports give regulators information to identify safety concerns and investigate potential defects. But a crash report does not necessarily reveal every step in an AI system’s decision-making. NHTSA also notes that information in initial reports can be incomplete or unknown and may later be updated.

A 2024 case involving Cruise showed another side of the problem. NHTSA reached a consent order with Cruise over incomplete reporting related to crashes involving its automated-driving systems. Cruise agreed to pay a 1.5 million dollars civil penalty and submit a corrective action plan. Reuters reported that one of the cases involved a pedestrian who had first been struck by another vehicle and was then hit and dragged by a Cruise robotaxi.

The case was about reporting rather than AI interpretability. But it highlighted the importance of having reliable information when automated systems are involved in crashes.

Why Knowing “Why” Matters

Consider a vehicle that suddenly brakes. A passenger might assume that it has detected a pedestrian or another obstacle. But the same action could result from the system misunderstanding its surroundings and triggering an emergency response.
From outside, the vehicle’s behaviour looks the same. For the person supervising it, the difference can determine whether they trust the system or intervene.

CW-Net is designed to make some of the concepts influencing the vehicle’s planning visible. The researchers integrated the system into the machine-learning planner rather than generating an explanation only after the decision had been made. They say this makes the explanations causally connected to the vehicle’s behaviour.

Autonomous vehicles detecting pedestrians
An autonomous vehicle detects approaching pedestrians, illustrating how AI-powered systems can identify road hazards and make driving decisions in real time. Image credit: MIT News

The system was trained using 130 million examples of self-driving scenes with labelled concepts. In tests with a Motional autonomous vehicle on a private track, the explanations helped safety drivers predict vehicle behaviour more accurately. A larger simulation study using real driving situations from Las Vegas produced similar results among non-expert participants.

That does not mean CW-Net prevents crashes. Its potential value is more immediate: it can give humans information that may help them recognise a failure and give engineers another way to investigate it.

India is Moving Towards More Connected Vehicles

India’s immediate road-safety challenge is still conventional road crashes. At the same time, India is introducing technologies that allow vehicles to exchange and process more information about their surroundings. In August 2026, the Ministry proposed phased implementation of vehicle-to-vehicle communication. Under the draft proposal, L, M and N category vehicles manufactured from October 1, 2028 would be required to have V2V systems conforming to AIS-230. The proposed systems would allow vehicles to exchange information including speed, position, direction and acceleration.

The government says such communication could support warnings for sudden braking, forward collisions, unsafe lane changes and approaching emergency vehicles. V2V is not autonomous driving. It does not transfer control of the vehicle to an AI. But it is part of a broader shift towards vehicles relying more heavily on sensors, software and communication systems.

Reuters also reported in June that India had removed licensing requirements affecting spectrum used by automotive radar and V2X technologies, potentially making it easier to deploy some crash-avoidance and connected-vehicle technologies. As these systems become more sophisticated, understanding their failures becomes important too.

The Indian Question

An autonomous vehicle operating in India would have to navigate roads shared by cars, buses, motorcycles, bicycles and pedestrians, often in complex and unpredictable situations. That does not mean Indian roads are inherently unsuitable for autonomous driving. It does mean that automated systems need to be tested in the environments in which they are expected to operate, including unusual situations that can challenge their perception and decision-making.

This is where technologies such as CW-Net could eventually have a role. If a vehicle responds incorrectly, an understandable record of what influenced its decision could help a safety operator recognise the problem. The same information could help engineers identify weaknesses in the system and work on correcting them. For regulators and investigators, such information could also become useful when reconstructing what happened after a crash.

The goal, however, should not be to make people trust autonomous vehicles simply because the systems can explain themselves. An explanation is useful only when it is accurate and helps a person make a better decision. A self-driving car will still have to make the right decision. But when it does not, being able to understand where the decision went wrong could help humans respond sooner—and help engineers make the next system better.

EP Staff is the editorial team at EdPublica, an independent media organisation focused on science, education, environment and public policy. The team produces evidence-based news, features, explainers and analysis on issues that shape society and everyday life.

Technology

Meta’s $18 Billion Child-Safety Reckoning: Can Social Media Be Made Safer for Teens?

Meta’s 18 billion dollars child-safety settlement marks a major shift in how social media platforms are being held accountable for protecting young users, putting renewed focus on screen time, platform design and age verification.

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Two teenagers using smartphones in a dimly lit room, highlighting concerns around social media use and child online safety.
As social media platforms face growing scrutiny over teen safety, new measures are putting greater responsibility on companies to create safer digital spaces for young users. Representational image. Image credit: Ron Lach/Pexels

Meta has agreed to pay up to 18 billion dollars to settle a sweeping legal challenge brought by nearly all U.S. states over allegations that Facebook and Instagram were designed to keep children and teenagers engaged despite risks to their wellbeing. Announced on August 26, the settlement ends a major federal trial in California and requires Meta to introduce significant changes to how teenagers use its platforms. Meta has not admitted wrongdoing.

More importantly, the agreement reflects a changing approach to online child safety: platforms, rather than parents alone, are increasingly being held responsible for how their products affect children.

What will Change?

Under the agreement, teenagers will receive a default two-hour daily limit across Facebook and Instagram, although parents can override it. Meta will also introduce a default Night Mode, restricting use between midnight and 6 a.m. Notifications will be muted during school hours, from 8 a.m. to 3 p.m., except for direct messages and safety-related alerts.

Teenagers will receive reminders after extended periods of use and at certain daily-use thresholds. Meta will also strengthen age-assurance measures aimed at keeping children under 13 off its platforms. The measures are expected to remain in place for 10 years, with independent oversight of Meta’s compliance.

Financially, the settlement could reach 18 billion dollars. About 12.7 billion dollars is guaranteed over 10 years, while another 5.3 billion dollars is conditional on TikTok and YouTube adopting specified safety measures and making corresponding payments.

Tackling the Screen Time

The lawsuits were not simply about teenagers spending too much time online. States accused Meta of designing features that encouraged prolonged engagement while allegedly downplaying risks to young users. They also raised concerns over the collection and use of personal information from children under 13. That puts the focus on the architecture of social media itself.

Recommendation systems, notifications, engagement metrics and endless feeds can influence how long users remain on a platform. The larger question is whether companies should be responsible when those design choices contribute to harm among young users. The settlement pushes the debate towards platform responsibility rather than individual responsibility.

A hand holding a smartphone displaying the Threads app logo, with the Meta logo visible in the background.
Meta faces growing scrutiny over child safety on its social media platforms, following an $18 billion settlement over allegations that Facebook and Instagram encouraged prolonged engagement among young users. Representational image. Image credit: Julio Lopez/Pexels

Is Two Hours Enough?

A daily limit may reduce screen time, but it does not necessarily address what happens during those two hours. What content teenagers encounter, how algorithms recommend it and how platforms respond to vulnerable users remain important questions.

Meta’s advertising-driven business model also remains intact. The settlement does not fundamentally change the commercial incentives behind user engagement. But can social media be made safer simply by limiting how long children use it?

The Age-verification Dilemma

The agreement also highlights another challenge: how platforms determine who is a child. Stronger age assurance can make it harder for children to bypass restrictions. But systems involving facial analysis, identity checks or other forms of age estimation can create new privacy risks.

Protecting children online, therefore, also requires deciding how much information platforms should collect to establish a user’s age.

A Test for the Industry

Although the settlement applies to Meta’s U.S. services, its implications extend beyond the company. Meta is calling on TikTok and YouTube to adopt similar safeguards, potentially pushing child-safety measures towards an industry-wide standard.

The settlement could ultimately prove to be less about the 18 billion dollars than about who bears responsibility for children’s digital lives. For years, parents have been asked to manage screen time, adjust settings and monitor what children see online.

Meta’s settlement suggests a different expectation: if platforms are built in ways that can affect children, the platforms themselves may have to change. The next decade will show whether that shift can produce a genuinely safer social-media environment for young users.

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Climate

The Giant Steel Gates Guarding the Netherlands from the Sea

The Maeslantkering is the Netherlands’ giant movable flood barrier, protecting Rotterdam and South Holland while keeping one of Europe’s busiest ports open.

Sebin Pious

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Maeslantkering, the Netherlands’ giant movable storm-surge barrier
The northern section of the Maeslantkering, the Netherlands’ giant movable storm-surge barrier near Rotterdam.. Credit: https://beeldbank.rws.nl, Rijkswaterstaat (Joop van Houdt)

The Maeslantkering uses two enormous movable steel gates to protect Rotterdam and South Holland from extreme storm surges while keeping the river open to ships.

Imagine a wall of water rising from the sea, threatening to flood low lying towns, farmlands, and entire cities. Now picture two massive steel arms, each as long as the Eiffel Tower, floating out from the riverbanks to join together and hold that water back.

This is not a scene from a movie. It is a real piece of infrastructure spanning the Nieuwe Waterweg river channel near Hoek van Holland in the Netherlands. Known as the Maeslantkering, or the Maeslant Barrier, it is the largest movable flood barrier on Earth. For more than three million people living in South Holland, including the port city of Rotterdam, these steel gates are the main defense against extreme ocean storms.

Maeslantkering: Why the Netherlands’ Giant Flood Barrier Matters

Why the Dutch Built a Gate Instead of a Wall The Netherlands has managed water for centuries, as nearly a third of the country sits below sea level. After a devastating North Sea flood in 1953, the Dutch government built a vast network of dams, dikes, and storm surge barriers across the country, known as the Delta Works.

However, the river route leading to Rotterdam created a practical problem. Rotterdam is home to Europe’s largest and busiest seaport. Blocking the river permanently with a fixed dam was impossible because cargo ships need round the clock access. The initial plan was to build higher earthen dikes along the riverbanks. But as engineers examined future sea level projections, they realized standard dikes would have to be enormous. Building them meant demolishing historic neighborhoods and disrupting communities for decades.

The solution was a different approach altogether: a storm surge barrier that stays open during normal weather to keep shipping lanes clear, but swings shut when severe storms approach.

How the Gates Work

The mechanics of the Maeslantkering are straightforward in design, but huge in scale. The barrier relies on two hollow steel gates parked in dry docks on opposite sides of the river.

When a major storm hits, hydraulic engines push the gates out into the waterway, where they float like barges until they meet in the middle. Once aligned, valves open and the gates fill with river water. As they gain weight, they sink onto a concrete bed built into the river floor.

As the gates lower, water rushes underneath them at high speed. This natural currents sweep away sand and silt so the structures rest flat against the riverbed without getting stuck on sediment. When the storm passes and ocean levels drop, pumps empty the water from inside the gates. The buoyant structures float back up and swing back into their docks, reopening the river to maritime traffic.

Automated Controls with Human Oversight

The operation of the Maeslantkering relies heavily on automation. The entire closure process is directed by a specialized computer system called the Decision Support System, known by its Dutch acronym BOS.

The software constantly monitors weather forecasts, incoming tides, and river flow rates. If calculations show water levels will rise 3 meters above normal in Rotterdam, the system initiates the closure process automatically. Leaving the trigger to software removes the risk of human delay or miscalculation during a sudden storm emergency.

Even with automation running the system, human engineers remain on site. Whenever severe weather threatens the coast, a technical team monitors the operations from a nearby control room, ready to take manual control if a system fault occurs.

Balancing Ships, Farms, and Rising Tides

Closing the barrier stops all ship traffic into Rotterdam, so shutting the gates is never done without cause. The barrier only closes during major storm events, though engineers run a routine test closure every September to keep the machinery and operational teams prepared.

As sea levels change and seasonal river flows shift, the Maeslantkering remains a critical piece of Dutch water management. It demonstrates how civil engineering can function alongside natural waterways, protecting millions of residents while keeping an essential trade route open to the world.

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Technology

Chinese Humanoid Robots Beat Human Records at Beijing Games

Chinese humanoid robots have surpassed long-standing human benchmarks in sprinting and jumping at the World Humanoid Robot Games in Beijing, showcasing rapid advances in robotic speed, balance and autonomous movement.

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Humanoid robot running during a demonstration of robotic movement
Humanoid robots are being tested for speed, agility and autonomous movement at international competitions. Representational image. Image credit: Pavel Danilyuk/Pexels

Chinese humanoid robots have beaten long-standing human athletic records at the second World Humanoid Robot Games in Beijing, showcasing how quickly machines are improving at running, jumping and coordinated movement. A robot completed the 100-metre sprint in 9.39 seconds on Saturday, faster than Jamaican sprinter Usain Bolt’s men’s world record of 9.58 seconds, set in 2009. Another robot recorded a 2.88-metre standing high jump, surpassing the 2.45-metre high-jump record set by Cuba’s Javier Sotomayor.

The performances are not official human athletics records. Robots compete under different physical conditions and their results are better understood as demonstrations of advances in robotics.

What are the World Humanoid Robot Games?

The World Humanoid Robot Games are an Olympic-style international competition designed specifically for humanoid robots. The event was first held in Beijing in 2025 and returned this year with a much larger field.

The five-day 2026 edition features more than 2,000 humanoid robots representing 666 teams from 16 countries. The competitions cover sports such as athletics, football, gymnastics, weightlifting, martial arts, dancing and tug-of-war.

The games are being held at Beijing’s National Speed Skating Oval, the venue known as the “Ice Ribbon” that was built for the 2022 Winter Olympics.

From running tracks to real-world tasks

The event is not limited to sporting competitions. Organisers have also introduced scenario-based challenges designed to test whether humanoid robots can perform useful tasks outside controlled sporting environments.

These include activities related to factories, hotels, homes, hospitals, emergency response and retail. Robots are expected to perform tasks such as handling objects, assisting in service environments and responding to practical situations.

This year’s 100-metre race has also been upgraded to allow fully autonomous robots, removing the need for direct human control during the event.

Speed is improving, but control remains a challenge

The record-breaking sprint also showed one of the problems humanoid robots still face: stopping.

After completing the 100 metres, some robots struggled to slow down and crashed into padded barriers. The incident highlighted the difference between achieving high speed and controlling a machine reliably at the end of a movement.

That distinction matters because the broader goal of humanoid robotics is not simply to make machines run faster than humans. Researchers and companies are trying to develop robots that can move through unpredictable environments, manipulate objects and complete tasks with minimal human intervention.

The Beijing games therefore serve as both a sporting spectacle and a test bed for a rapidly developing technology. The record-breaking performances show what humanoid robots can do under controlled conditions; the scenario-based events test whether those abilities can translate into useful work in the real world.

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