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The Tight Connection Between Reinforcement Learning and Autonomous Driving

The Mercedes-Benz Drive Pilot recently became the first Level-3 autonomous driving system to be certified for US roads. Amidst reports that expect the market of self-driving vehicles to gain 62.4 million units by 2030, Mercedes has set the perfect example for the bright future of autonomous driving, offering stunning autonomous features like distancing, lane keeping, and active steering.

But what makes these autonomous vehicles so capable? What machine learning techniques do they employ? What advanced driving assist systems do they depend on? 

The Rise of Autonomous Driving

Vehicle production (and sale) witnessed a tremendous decline in 2020 across the world owing to the pandemic. High-interest rates, poor demand, and several bottlenecks in global supply chains led to several hurdles in the testing and launching of autonomous vehicles. Chip shortages made it difficult for any new investment in advanced technology, bringing the autonomous vehicle market to a standstill.

But as new trends emerge and OEMs experience recovery in vehicle sales (expected to reach 83.6 million units in 2023 globally), the autonomous driving landscape is witnessing tremendous traction. Despite being vulnerable to recession headwinds, the autonomous vehicle market is expected to grow tremendously in the coming year(s), and there are several reasons for this:

  • Advancements in driving assist systems
  • The relentless focus of governments to enhance vehicle safety
  • The many initiatives being taken by OEMs to provide advanced safety features
  • The several significant enhancements to the capability and reliability of sensors, cameras, and vehicle-to-everything communication
  • The rise of the overall development of smart cities and the adoption of smart technologies in everyday life

The Role Reinforcement Learning Plays in the Success of Autonomous Driving

As level-3 systems make their way into the market, auto manufacturers vying to get into the space need to have a better understanding of the technologies that make autonomous vehicles a (risk-free) reality. Reinforcement learning is one of the many technologies that play a huge role in the success of autonomous driving. So, what is reinforcement learning?

An AI technique that trains neural networks to perform a task through trial and error, reinforcement learning is touted to be a breakthrough technology that makes driverless cars what they are. Since self-driving cars must continuously consider several factors such as speed limits, pedestrian crossing, drivable zones, etc., the technology allows vehicle manufacturers to attain the safety levels needed for deploying autonomous cars on a large scale.

Developers use reinforcement learning to devise a method to reward desired behaviors and punish negative behaviors. By assigning positive values to desired actions and negative values to undesired ones, it seeks long-term and maximum overall reward to achieve an optimal solution, enabling the model to avoid the negative and seek the positive.

Autonomous cars are trained for thousands of conditions with highly difficult simulations before they’re deployed in the real world. During training, the car learns by taking a certain action in a certain state and either gets penalized or rewarded based on the outcome. This process is carried out repeatedly, and each time, the car updates its memory of rewards – thus improving its ability to make favorable decisions.

Reinforcement learning models explore and interact with the environment and present a list of actions from data based on three variables:

  • State, or the current situation of the car – say its position on the road.
  • Action, or the possible moves the car can make.
  • Reward, or the feedback the car receives when it takes a certain action.

By offering capabilities that help tackle a wide variety of challenges on the road both safely and effectively, reinforcement learning frameworks offer the ideal features to improve autonomous performance.

Merging Traffic – A Use Case

To understand the role of reinforcement learning, let’s take an example. Consider a situation where an autonomous vehicle must merge into traffic and deviate onto a main road from a ramp road. Because of the many challenges involved with freeway merging, including vehicles traveling at high speeds, frequent lane changing, and drivers driving with various styles, reinforcement learning helps in effectively capturing merging scenarios and planning the vehicle’s actions based on a detailed analysis of possible risks across loss of control, skidding, and jackknife accidents.

So, how does reinforcement learning do this? Reinforcement learning models interact with the real-world environment and collect data to optimize actions. It investigates the potential safety risks of autonomous vehicles in traffic merging and builds a conflict model, accounting for real-world uncertainties and variations across various crash-contributing factors, and presents probabilistic distributions that are calibrated on the empirical freeway data. By earning rewards for positive actions in ramp merging operations, reinforcement learning reduces the possibility of traffic conflicts and crashes and partially (or fully) eradicates negative factors – thus reducing the risk of accidents.

The Future of the Auto Industry Hinges on Autonomous Vehicles

It’s 2023, and news articles on autonomous driving are topping headlines across the world. With Hyundai and Kia preparing to launch Level-3 autonomous vehicles and Tesla’s full self-driving beta pool ballooning to over 400,000 users in January 2023, the future of the auto industry hinges on the success of autonomous vehicles in many ways.

But turning the autonomous driving dream into reality is hard work. Several technologies come into play, reinforcement learning being one of them, that have to be carefully and successfully implemented.

As the fascination for self-driving increases and as more and more cars deliver on their self-driving promises, the value they generate is immeasurable. In Elon Musk’s words, “when a car becomes fully autonomous, that is a value increase in the fleet. That might be the biggest asset value increase of anything in history. Yes.”

Are you ready for the future of autonomous driving?

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