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An insight into crash avoidance and overtaking advice systems for
Autonomous Vehicles: A review, challenges and solutions 

Published in Elsevier
Volume: 104
Issue: 104406
Pages: 1 - 25

Emergence of communication technologies made the automotive industries across the globe to embrace
Advanced Driver Assistance Systems (ADAS) by considerable investments to ensure accident-free travel,
reduction of pollution, fuel conservation. ADAS achieves its goals by integrating complex subsystems such as
obstacle avoidance, overtaking advice, lane changing assistance, planning shortest routes, parking assistance,
automatic gear shifting, etc., using the emerging technologies. This article emphasizes the road safety aspect
of the ADAS by exploring Crash Avoidance and Overtaking Advice (CAOA) subsystems. Existing studies have
a noticeable lack of connectivity between various aspects of CAOA subsystems. This review deeply explores
and connects CAOA subsystems like road geometries, road debris, obstacle avoidance algorithms powered by
Artificial Intelligence (AI), overtaking advice systems, perception challenges of human drivers in various light
and weather conditions, driver inattention and misjudgments, vehicle blind-spots, vehicle parameter analysis,
performance of vision sensors, in-vehicle computers, driver–vehicle interactions, Vehicle to Infrastructure (V2I)
technologies. This article emphasizes the three primary performance metrics of the ADAS, namely accuracy,
response time and robustness. Finally, this article discusses a typical functional architecture and gaps identified
in existing studies. This article is structured to assist like-minded researchers, who work on CAOA systems for
road safety.

About the journal
JournalData powered by TypesetEngineering Applications of Artificial Intelligence
PublisherData powered by TypesetElsevier
Open AccessNo