The rise of self-driving cars represents a significant technological advancement in the automotive industry. These autonomous vehicles utilize a combination of advanced sensors, artificial intelligence, and intricate algorithms to navigate and make decisions on the road without human intervention.
However, concerns about the safety of self-driving cars persist. One major issue revolves around the ability of these vehicles to accurately perceive and respond to complex and dynamic environments.
Factors like adverse weather conditions, unexpected road hazards, and erratic human drivers pose significant challenges for autonomous systems. Critics argue that these systems must reach exceptionally high levels of precision and reliability before they can be deemed safe for widespread use.
Now, a team of researchers from NYU Tandon School of Engineering has introduced an algorithm termed Neurosymbolic Meta-Reinforcement Lookahead Learning (NUMERLA) that promises to address the enduring challenge of effectively adapting to unpredictable real-world scenarios while upholding safety standards, TechXplore reported.
Conducted by Quanyan Zhu, an associate professor of electrical and computer engineering at NYU Tandon, along with Ph.D. candidate Haozhe Lei, this research is now available on the preprint server arXiv.
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NUMERLA Bolsters Self-Driving Cars’ Ability to Negotiate Complex Scenarios
The researchers noted that the advent of artificial intelligence (AI) and machine learning has significantly bolstered self-driving cars’ ability to negotiate complex scenarios.
This progress empowers them to process extensive sensory information, decode intricate surroundings, and maneuver through city streets while following traffic rules.
However, the transition from controlled environments to the unpredictability of real-world traffic renders these vehicles potentially susceptible to performance lapses, which could cause accidents and other risks. NUMERLA sets out to harmonize safety and adaptability.
This algorithm achieves this equilibrium by dynamically fine-tuning safety parameters in real time. It allows self-driving cars to adeptly navigate unfamiliar and ever-changing scenarios, all while maintaining safety, according to the researchers.
NUMERLA’s operational process unfolds as follows: When faced with a changing environment, a self-driving car uses accumulated data to fine-tune its understanding of the present circumstances.
Based on this analysis, the car anticipates its upcoming performance within a specified period. It then identifies relevant safety criteria and adjusts its knowledge base correspondingly.
The vehicle’s operating protocol is fine-tuned using lookahead optimization while adhering to safety constraints. This results in a strategy that may not be optimal but is proven safe during real-time operation.
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Lookahead Symbolic Constraints
One of the distinctive features of NUMERLA is its integration of lookahead symbolic constraints. By making informed speculations about its future mode and incorporating symbolic safety considerations, the self-driving car can respond to new and unforeseen situations while ensuring safety.
The researchers deployed NUMERLA in a computerized platform emulating urban environments in a series of tests. According to the team, its performance was specifically evaluated in handling jaywalkers, and it demonstrated superior capabilities compared to other algorithms in these scenarios.
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