New research suggests that AI systems can be made safer and more cautious drivers by being assigned neural traits similar to what humans experience when they feel fear.
A new kind of 'fear-inspired' reinforcement learning technique, called FNI-RL (Fear-Neuro-Inspired Reinforcement Learning), is proving useful in making self-driving cars safer.
The researchers found that FNI-RL performed much better than other AI agents and even human drivers in various driving scenarios.
In one short-distance driving scenario, FNI-RL showed improvements ranging from 1.55 to 18.64 percent in driving performance compared to other autonomous systems.
In a longer simulated driving test, FNI-RL improved driving performance as much as 64 percent compared to other autonomous systems.
FNI-RL was more likely to reach its target lane without any safety violations, including collisions and running a red light.
The researchers also conducted experimental tests of FNI-RL against 30 human drivers, and FNI-RL outperformed humans in all three scenarios.
More work needs to be done before this system can be implemented in autonomous vehicles, but the results show promise for making self-driving cars safer.
Source : https://spectrum.ieee.org/autonomous-vehicle-safety-defensive-driving
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