We have all heard of autonomous vehicles, especially the cars they drive on their own. But do you know that many of the same principles apply to self-direction planes? As the Autonomous Transport Age becomes a reality, critical questions arise about security and decision -making: How are algorithms for coincidence in autonomous systems such as cars and aircraft calculated? How do these safety systems provide during unpredictable circumstances? How do imperfect sensors affect the overall safety of vehicles? How is future uncertainty modeled? And, most importantly, how can artificial intelligence (AI) be trained to handle rare, high -ranging scenarios? To explore these questions, I interviewed Mykel Kochenderfer, an Associate Professor of Aeronautics and Astronautics and Associate Professor, Conducting, Computer Science at Stanford University and Director of Stanford Intelligent Systems (SISL).
Professor Kochenderfer’s work centers in the creation of advanced algorithms and analytical methods for decision -making in dynamic, unsafe environments. His team focuses on high -stock systems such as air traffic control, unmanned aircraft and automated vehicles, where safety and efficiency are primary. By using possible models and optimism techniques, they aim to design strong systems capable of adapting to real -world variability.
Modeling random in autonomous systems
When asked about coincidence in autonomous systems, Professor Kochenderfer emphasized the inherent variability of real -world environments. “The systems we build, whether for planes or cars, must interact with the real world,” he explained. “And the real world has tremendous variability. There are other drivers on the streets and pedestrians, and there is this essential coincidence. People do not always walk straight. Cars do not always follow the speed limit.”
To account for this, the team uses probabilistic models that set different weights in possible results, optimizing decision -making strategies based on these probability. For example, “most of the time, aircraft fly straight, but sometimes they turn left or right. It is important to weigh different possible future future,” he noted. Their methodologies optimize objectives such as achieving a destination safely by minimizing passenger discomfort.
Data collection is a critical part of this process. For aircraft, radar tracks by the federal aviation administration can be used to build statistical behavioral models during meetings. For driving, publicly available data from organizations like Waymo serve as valuable resources for modeling naturalist behaviors.
The role of imperfect sensors
Inexperical sensors are another challenge in autonomous systems. “When you have imperfect sensors, your understanding of the world will be imperfect,” Professor Kochenderfer explained. This makes it difficult to predict future events and complicate decision -making.
To address these restrictions, decision -making strategies must be stronger and conservative, calculating the sensor’s noise and occlusions. “We try to plan in a way that accepts these restrictions,” he said. “While it is challenging to create a system that is 100% safe, we work to ensure a very high level of security by identifying weaknesses and characterizing expected levels of failure.”
Modeling of uncertainty in the future
A central question in the design of autonomous systems is how to model future uncertainty. Professor Kochenderfer described the use of pre -set probability distribution to reflect the observed data. Metrics like log chances are used to measure how well these models capture uncertainty. An additional method of validity involves simulating trajectories that are indistinguishable from real -world data, similar to a change of Turing test in it. “Simulations that look realistic for human experts can help decide the belief that our models are appropriate,” he said.
Including human experience in training he
Human expertise remains essential in designing systems capable of treating rare scenarios or skirts. According to Professor Kochenderfer, “Building these systems requires data and judgment of experts.” By automating optimizing decision -making strategies and proving them against human experiences, the inconsistencies between optimized system behaviors and human judgment can be analyzed to refine models.
EDGE cases present an important challenge because it is impossible to prove any scenario against human judgment. The design process includes the prioritization of the human effort to ensure security in critical situations. This repetitive approach balances the automation and human contribution to create more reliable systems.
Balancing safety and operational efficiency
The balance between safety and operational efficiency is delicate. Excessive systems that often apply strong brakes, for example, can irritate users or even cause secondary accidents. In contrast, insufficient care can endanger security. “Getting this fair balance is really complicated,” said Professor Kochenderfer.
To address this complexity, his team has developed tools that help designers weigh multiple metrics within the safety and efficiency categories. These tools aim to simplify the process of creating systems that are effective and practical for real -world use.
Regulatory and ethical considerations
The role of government policy is critical in fostering safe and responsible innovation. “The Department of Transport, for example, has a challenging job,” noted Professor Kochenderfer. “Their top priority is security, and they have achieved an extraordinary aviation security record. But they also recognize the potential of developing technologies to bring additional safety benefits.”
Balancing innovation with adjustment is not an easy task, especially for developing technologies like it. Policymakers should encourage advances by preventing premature deployment. Professor Kochenderfer emphasized the importance of a measured approach, learning from the mistakes of the past and adopting additional steps to create confidence in autonomous systems.
The inspiration of the next generation
As the interview ended, Professor Kochenderfer shared tips for new students interested in the crossing and technology intersection. “Develop good study habits and cultivate an interest in mathematics, statistics and optimization,” he said. “Basic mathematics and it is extremely fun and creative. Moreover, learning how to work effectively in teams is essential, as these technologies require cooperation on a massive degree. “
cONcluSiON
The development of autonomous systems is a unique set of challenges and opportunities. Utilizing probabilistic modeling, powerful algorithms and a mix of human expertise and data -driven optimism, researchers like Professor Kochenderfer are paving the way for safer, more efficient transport systems. As we stand on the verge of a transformative era in mobility, their work underlines the importance of innovation based on rigorous safety and validity.