How can you use AI to make transportation safer with reinforcement learning?

Well. That's one of my favorite topics in Artificial Intelligence. AI and reinforcement learning (RL) can make transportation safer by teaching systems to make better decisions. In autonomous vehicles, RL enables real-time adjustments for speed and positioning, learning from past outcomes to improve future performance. For services like traffic management, RL algorithms optimize signal timings to ease congestion, minimizing accident risks. Of course, there are many challenges, like ensuring reliability amidst unpredictable road conditions and integrating with legacy infrastructures. But for me, it is clear that RL has been central to developing safer, more efficient transportation networks.

RL certainly would appear to have an important place in AI safety. RL is not always a force for positive outcomes however, an example would be a bully pushing another child to do something they don’t want to and being rewarded with compliance. What we need the agent to understand is the difference between positive and negative reinforcement, a much harder but necessary ask. I appreciate the idea of rewards and disincentives with the goal to gain more rewards over time.

AI can be used to make transportation safer with reinforcement learning by training models to make better decisions based on rewards and punishments. RL can be used for obstacle avoidance and decision-making in autonomous driving. However, safety concerns are usually raised when deploying RL in the real world. To address these concerns, researchers are developing safe RL algorithms that can improve the safety performance of DRL models 3

Reinforcement Learning (RL) is invaluable in transportation due to its adaptability to uncertainties and complexities. In dynamic scenarios like autonomous driving, RL enables vehicles to learn safe, efficient navigation amidst varying conditions. It balances factors like speed, comfort, and fuel consumption, crucial for optimal performance. Traffic controllers benefit from RL's ability to optimize flow, easing congestion considering diverse driver behaviors. In smart mobility systems, RL coordinates transportation modes, balancing supply and demand while mitigating environmental impact. RL's capacity to learn from experiences equips transportation systems to evolve, ensuring adaptability in a changing landscape.

There's a few "scifi" tangential responses to this. Such as having an optimised city where traffic is non-existent because it's controlled by AI - all the way to automated drivers that can take anyone in any condition from A to B (but less like Tesla and more like Knight Rider). In terms of RL though, RL in transportation parallels a seasoned driver. Learning from countless journeys. Adapting to new routes. Potentially learning new things and optimizations. Just as experience refines human choices, RL fine-tunes systems for optimal and sustainable mobility.

Reinforcement learning is valuable for transportation because it enables autonomous systems to learn and adapt their behaviors based on real-world experiences, making them more capable of navigating complex traffic scenarios and enhancing safety.

Concerning autonomous driving, the primary challenge will be dealing with entirely anomalous situations. Not only due to the multitude of different variables that may arise but also because, in the future, human drivers will learn to "troll" autonomous vehicles. There will be a need to handle modified traffic signals meant to deceive sensors and drivers who understand the distance they should maintain to manipulate autonomous vehicles into yielding the right of way. In essence, in the not-so-distant future, autonomous vehicles won't only face the same challenges as human drivers but will also encounter problems deliberately created by human drivers who know how to act to prioritize themselves over autonomous vehicles.

ML Machine Learning has both supervise learning & Reinforcement Learning🎖️🏆. I admire in AGV's now a days using in manufacturing can control the process flow. One of our client ask for process improvement to carry 10 tonns of spares and assembly Bins trolley hooked. We need complex algorithm data analysis. In RL, the agent explores the environment by exploring it without any human intervention. It is the main learning algorithm that is used in Artificial Intelligence. But there are some cases where it should not be used, such as if you have enough data to solve the problem, then other ML algorithms can be used more efficiently. Alert! RL algorithm is that some parameters may affect the speed of the learning, such as delayed feedback.

Humans. Humans are the challenge. Transportation RL intertwines with unpredictable real-world dynamics and human behaviours. Ensuring reliable RL systems in transportation means constantly navigating a tightrope between efficiency and safety.

Looking at AI and reinforcement learning from a psychological perspective is important. Key factors in addressing challenges to build towards solutions effectively are: ⚙️ Cognition - Focus on understanding human perception, attention, and abstraction 🤝🏾 Social dynamics - Tap into human social behavior to leverage cooperativeness 🎯 Motivation - Incentivize safety and ethics through rewards or penalties that align with human values 🎓 Development - Progress agent policies through stages similar to human learning This creates autonomous transportation that aligns with human values and capabilities! 🚘 ✅

After the agent has undergone training, it becomes essential to assess its effectiveness through appropriate evaluation metrics. The ability for online learning and adaptation is vital to ensure the system remains responsive to changing conditions. It is imperative to always prioritize safety and adherence to regulatory requirements in the transportation domain. Furthermore, fostering collaboration and engagement with stakeholders, local authorities, and communities plays a significant role in aligning the RL-based transportation system with the needs and concerns of the people it serves. Lastly, it's important to incorporate scalability into the plan, as transportation systems naturally evolve and expand over time.

Traffic comes from humans who go to work or knock off from work at the same time. As important as it is to monitor traffic and optimise for that, it is also essential to integrate that information with the neighbouring offices and buildings to coordinate office hours.

Autonomous Vehicles/Driving Traffic Management -> Signal Control Smart Mobility These applications showcase RL's versatility in enhancing various aspects of transportation, from vehicle control to traffic flow and integrated mobility solutions.

1. Autonomous Driving: RL helps vehicles navigate complex environments, making decisions on speed, lane changes, and interactions with other vehicles, ensuring safe and efficient travel. 2. Traffic Management: RL optimizes traffic signal timings, reducing congestion and improving traffic flow. It dynamically adapts signals based on real-time traffic conditions. 3. Smart Mobility Systems: RL coordinates various transportation modes (buses, trains, rideshares) based on demand, reducing environmental impact and providing convenient, sustainable mobility options.

The application of Reinforcement Learning (RL) in Predictive Maintenance allows for the anticipation of potential vehicle part failures and the timely scheduling of maintenance. This not only minimizes downtime but also prolongs the lifespan of the vehicle.

In the use case of transportation, RL would probably be trained on simulations before any sort of real-life deployment. It is crucial to ensure that the data used to create those simulations is accurate and unbiased, to ensure the RL models converge to a real optimal solution, and not one perpetuating existing flaws in the transportation system being optimized.

It’s my pleasure to gain knowledge from all your responses. In my experience, I believe implementing AI for safety is a complex task. With expertise in ML, robotics & transportation engineering, implementation of RL can enhance development of self-driving cars. For example: In the Wayamo’s self-driving car, the power of RL is used to train this autonomous vehicle to make safe driving decisions hence making transportation safer. Also as a proof, their vehicles have accumulated millions of autonomous miles, demonstrating significant progress in achieving safe and reliable self-driving technology. In addition, it is also crucial to collaborate with experts in the field to ensure that the system meets safety & legal requirements too.

RL combined with AI can make a swift different in the world of transportation to make it safer. With the intelligence embedded in vehicles through multiple sensors, we can create aggregated sets of information created out of the individual behaviors of each vehicle, and analyze the impacts of that behavior in a situation (real/simul). The results could be fed into the RL models, to enhance the learning of the fleet of connected vehicles.That way, we can create a continuous model of learning for the vehicles that will be based on the environment (congestion/traffic, lights, obstacles) & drivers behavior (decision, car response), which will improve the decision making for the cars and the drivers that will be in a similar situation.