From: lexfridman
Autonomous vehicles represent one of the most promising yet challenging aspirations within the field of AI. They have the potential to redefine personal and public transport by enhancing safety, reducing congestion, and enabling greater mobility for all. However, one of the core hurdles these systems face is embedding what we, as human drivers, consider "common sense" into their operational frameworks.
## The Complexity of Driving Scenarios
In the journey towards fully autonomous vehicles, developers face the intricate challenge of handling real-world complexities and edge cases. Common driving scenarios might seem straightforward for AI when the environment is controlled and predictable, but the reality is far from static. Unforeseen situations, such as a pedestrian suddenly appearing in the street or unexpected debris on the road, test the limits of an autonomous vehicle's problem-solving abilities and its "common sense" understanding<sup><a class="yt-timestamp" data-t="01:10:02">[01:10:02]</a></sup>.
### The Role of Perception and Decision Making
Current autonomous vehicles utilize a combination of sensors and algorithms to perceive the environment, often leveraging technologies like lidar, radar, and cameras. However, the perception systems of many self-driving cars, especially those dependent on vision alone, are still developing their capacity to distinguish, interpret, and respond to all types of obstacles accurately<sup><a class="yt-timestamp" data-t="01:11:01">[01:11:01]</a></sup>. The vehicles may err on the side of caution, leading to unpredictable and overly conservative driving behaviors, which can confuse and frustrate nearby human drivers<sup><a class="yt-timestamp" data-t="01:11:31">[01:11:31]</a></sup>.
### Learning and the Long Tail Problem
Autonomous driving systems must address the issue of the "long tail" of rare and novel driving situations. This challenge reflects the vast array of potential scenarios a vehicle might encounter that were not included in the training data. As a result, engineers and AI researchers are exploring methods for systems to learn new concepts and adapt their "understanding" over time, much like humans do. This endeavor continues to be a significant hurdle that speaks directly to the need for incorporating [common sense in artificial intelligence](#Common-Sense-in-Artificial-Intelligence)<sup><a class="yt-timestamp" data-t="01:10:39">[01:10:39]</a></sup>.
## The Need for Common Sense
The inclusion of common sense is paramount. It encompasses a range of implicit knowledge that humans use unconsciously, which current AI systems largely lack. This knowledge includes understanding physics at a basic level (e.g., objects in motion tend to stay in motion), recognizing the intentions of other drivers, and predicting pedestrian behaviors. The absence of such logical reasoning and intuitive understanding can lead to missteps in navigating the unpredictably dynamic environment that is real-world traffic<sup><a class="yt-timestamp" data-t="01:17:01">[01:17:01]</a></sup>.
## Bridging the Gap with AI Advancements
Efforts within the field are underway to address shortcomings in current AI systems' ability to enact common sense. These methods could include developing more sophisticated mental models equivalent to human cognitive processing and applying these models in a way that mirrors human perception<sup><a class="yt-timestamp" data-t="01:20:30">[01:20:30]</a></sup>. As AI continues to evolve, the integration of unsupervised learning techniques and improvements in [[ai_and_machine_learning_in_autonomous_driving]] could also contribute to breakthroughs in common-sense reasoning capabilities<sup><a class="yt-timestamp" data-t="01:22:21">[01:22:21]</a></sup>.
## Conclusion
While the journey to full autonomy in vehicles is filled with challenges, such as [[challenges_of_autonomous_vehicles_and_trust]] and [[autonomous_vehicles_and_societal_impact]], the pursuit of integrating common sense into AI remains crucial. This will not only advance the safety and efficiency of autonomous vehicles but also foster public confidence in these transformative technologies.
For further exploration of this field and related topics, consider delving into the broader implications of [[autonomous_vehicles_and_selfdriving_technology]] and how these advancements may shape future urban landscapes.