Skip to main content

Scientists Discover Way to Send Information into Black Holes Without Using Energy

3 comments

Scientists Finally Discovered Why Self-Driving Cars Crash And the Answer Could Change Everything

Self-driving cars are no longer a futuristic dream. They are already operating on roads in cities around the world, from London to San Francisco. These vehicles promise safer roads, fewer traffic jams, and greater convenience. However, one major challenge continues to hold back widespread public trust: when a driverless car crashes, it is often difficult to understand exactly why it happened.

Now, researchers have developed a new way to answer that question. For the first time, scientists have created algorithms that can automatically explain why a self-driving car made a mistake and crashed. The breakthrough could help engineers improve autonomous vehicles and make them safer for everyone.

The research was presented at the 2026 IEEE International Conference on Robotics and Automation by a team from King's College London.

The Growing Presence of Self-Driving Cars

Autonomous vehicles are becoming increasingly common across the globe. Technology companies and car manufacturers are investing billions of dollars into developing vehicles that can drive without human intervention.

These cars use cameras, sensors, radar systems, and artificial intelligence to understand their surroundings and make driving decisions. They can recognize pedestrians, detect traffic signs, avoid obstacles, and navigate busy streets.

While the technology has made significant progress, accidents still happen. Some crashes have raised serious concerns about road safety and have sparked public debates about whether autonomous vehicles are truly ready for widespread use.

One of the biggest problems is that when an accident occurs, manufacturers often struggle to provide a clear explanation of what caused it.

Why Understanding Crashes Is So Difficult

Traditional methods used to analyze self-driving car failures focus mainly on statistics. These methods can identify patterns and estimate how likely a similar failure might happen again in the future.

However, they cannot always answer a more important question: What exactly caused this specific crash?

According to Dr. Khen Elimelech, leader of the Autonomous Robots Lab at King's College London and lead author of the study, traditional approaches can only provide limited insights.

A self-driving vehicle constantly processes huge amounts of information while driving. Every second, it observes nearby vehicles, pedestrians, road markings, traffic signals, weather conditions, and many other factors.

When a crash happens, there may be thousands of possible events that contributed to it. Finding the true cause can be like searching for a needle in a haystack.

In some situations, an object detected miles before the accident may trigger a chain of decisions that eventually leads to a collision. This complexity makes identifying the root cause extremely challenging.

A New Approach Called "Actual Causality"

To solve this problem, the researchers turned to a concept known as "actual causality."

Instead of simply measuring how often failures occur, actual causality examines past events and works backward to determine exactly which factors caused a particular outcome.

Think of it like a detective investigating a crime scene. Rather than looking at general crime statistics, the detective carefully reconstructs the sequence of events to identify what truly happened.

The new algorithm does something similar for self-driving cars.

It analyzes every step leading up to a crash and determines which events were directly responsible for the failure. This allows researchers to understand not only that a mistake occurred, but why it occurred.

Although actual causality has been tested before in simpler AI systems used for image classification, this is the first time it has been applied to the far more complex world of autonomous vehicles and AI-powered physical systems.

From Predicting Crashes to Explaining Them

The research builds on earlier work from the same team.

Previously, the researchers developed a method called "falsification," which helps identify rare situations that could cause a self-driving car to fail. These scenarios are often difficult to discover through ordinary testing because they occur so infrequently.

The earlier algorithm could find dangerous situations before they happened.

The new research takes the next step by analyzing those dangerous situations after they occur and explaining what caused them.

This combination gives engineers a powerful tool. First, they can discover hidden weaknesses in autonomous systems. Then, they can understand the exact reasons those weaknesses led to failure.

Such insights could help manufacturers improve vehicle software and prevent similar accidents in the future.

Finding the Root Cause Faster

One major challenge is the enormous number of possible causes behind a crash.

A self-driving vehicle makes countless decisions during every journey. Evaluating all of these decisions requires significant computing power and time.

To overcome this problem, the researchers developed what they call a "responsibility-guided" search algorithm.

This system acts like an intelligent investigator. Instead of examining every possible factor equally, it focuses on the events most likely to be responsible for the crash.

As a result, the algorithm can identify explanations using far less computational effort than previous methods.

According to the researchers, this dramatically improves efficiency while still producing reliable explanations.

In practical terms, it means investigators and engineers can understand accidents much faster, allowing safety improvements to be implemented more quickly.

Building Trust in Autonomous Technology

For self-driving technology to become a normal part of everyday life, people need confidence that it is safe and accountable.

When human drivers cause accidents, investigators can often determine what happened through witness statements, road evidence, and driver actions.

With AI-powered systems, the decision-making process can sometimes appear like a "black box," making it difficult to understand how the vehicle reached a particular conclusion.

This lack of transparency can reduce public trust.

The researchers believe explainable AI is essential for solving this problem. If manufacturers can clearly explain why an autonomous vehicle made a mistake, regulators, engineers, and the public will have greater confidence in the technology.

Being able to understand failures is just as important as preventing them.

Beyond Self-Driving Cars

Although the study focused on autonomous vehicles, the potential applications extend far beyond transportation.

Many modern technologies are becoming increasingly dependent on artificial intelligence. From industrial robots and medical systems to smart infrastructure and assistive devices, AI is playing a larger role in daily life.

The researchers believe their approach could help explain failures in many different AI-powered physical systems.

One future possibility is the development of autonomous assistive robots in care homes. These robots may one day help elderly people with everyday tasks and provide support for caregivers.

Before such systems can be widely adopted, they must be reliable, safe, and transparent. The ability to explain why something went wrong could be crucial in ensuring public acceptance.

The Road Ahead

Self-driving cars have the potential to transform transportation, but safety remains the industry's biggest challenge.

This new research represents an important step toward making autonomous vehicles more understandable and trustworthy. By identifying the true causes of crashes, engineers can learn from mistakes and design better systems.

As AI becomes more deeply integrated into society, understanding why intelligent machines make certain decisions will become increasingly important.

The ability to explain failures may ultimately prove just as valuable as the ability to prevent them—helping create a future where humans can confidently share the road, and many other aspects of life, with intelligent machines.

Comments

Post a Comment

Popular

Scientists Discover Way to Send Information into Black Holes Without Using Energy

For years, scientists believed that adding even one qubit (a unit of quantum information) to a black hole needed energy. This was based on the idea that a black hole’s entropy must increase with more information, which means it must gain energy. But a new study by Jonah Kudler-Flam and Geoff Penington changes that thinking. They found that quantum information can be teleported into a black hole without adding energy or increasing entropy . This works through a process called black hole decoherence , where “soft” radiation — very low-energy signals — carry information into the black hole. In their method, the qubit enters the black hole while a new pair of entangled particles (like Hawking radiation) is created. This keeps the total information balanced, so there's no violation of the laws of physics. The energy cost only shows up when information is erased from the outside — these are called zerobits . According to Landauer’s principle, erasing information always needs energy. But ...
3 comments
Read more

Black Holes That Never Dies

Black holes are powerful objects in space with gravity so strong that nothing can escape them. In the 1970s, Stephen Hawking showed that black holes can slowly lose energy by giving off tiny particles. This process is called Hawking radiation . Over time, the black hole gets smaller and hotter, and in the end, it disappears completely. But new research by Menezes and his team shows something different. Using a theory called Loop Quantum Gravity (LQG) , they studied black holes with quantum corrections. In their model, the black hole does not vanish completely. Instead, it stops shrinking when it reaches a very small size. This leftover is called a black hole remnant . They also studied something called grey-body factors , which affect how much energy escapes from a black hole. Their findings show that the black hole cools down and stops losing mass once it reaches a minimum mass . This new model removes the idea of a “singularity” at the center of the black hole and gives us a better ...
Post a Comment
Read more

How Planetary Movements Might Explain Sunspot Cycles and Solar Phenomena

Sunspots, dark patches on the Sun's surface, follow a cycle of increasing and decreasing activity every 11 years. For years, scientists have relied on the dynamo model to explain this cycle. According to this model, the Sun's magnetic field is generated by the movement of plasma and the Sun's rotation. However, this model does not fully explain why the sunspot cycle is sometimes unpredictable. Lauri Jetsu, a researcher, has proposed a new approach. Jetsu’s analysis, using a method called the Discrete Chi-square Method (DCM), suggests that planetary movements, especially those of Earth, Jupiter, and Mercury, play a key role in driving the sunspot cycle. His theory focuses on Flux Transfer Events (FTEs), where the magnetic fields of these planets interact with the Sun’s magnetic field. These interactions could create the sunspots and explain other solar phenomena like the Sun’s magnetic polarity reversing every 11 years. The Sun, our closest star, has been a subject of scient...
Post a Comment
Read more