Authors :
Thushal Babukumar; Raja Reddy
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/44COuAj
DOI :
https://doi.org/10.5281/zenodo.7902142
Abstract :
In recent years, the automotive industry has
witnessed a surge in the development of advanced vehicle
technologies that heavily rely on complex algorithms for
decision making. These technologies include advanced
driver assistance systems (ADAS), autonomous driving
systems, and connected vehicle systems, among others.
While these technologies have the potential to improve
safety, reduce accidents, and enhance the overall driving
experience, they also pose significant challenges related to
their reliance on complex algorithms. The increasing
dependency on complex algorithms for decision making
in vehicle technology raises several concerns related to
the reliability, transparency, and accountability of these
systems. One of the primary concerns is the potential for
errors or malfunctions in the algorithms that could lead
to accidents, injuries, or fatalities. Another concern is the
lack of transparency in how these algorithms make
decisions, which can make it difficult for users to
understand and trust the technology. To address these
concerns, there is a need for greater collaboration
between industry stakeholders, regulators, and
researchers to develop robust testing and validation
processes for complex algorithms used in vehicle
technology. Additionally, there is a need for greater
transparency and accountability in how these algorithms
make decisions, such as the use of explainable AI
techniques that can provide insights into the decisionmaking process. Overall, the growing dependence on
complex algorithms for decision making in vehicle
technology presents both opportunities and challenges for
the automotive industry. This paper aims to details by
addressing the concerns related to these algorithms,
stakeholders can work towards realizing the full potential
of these technologies while ensuring safety and reliability
for users.
Keywords :
Advance Driver Assist Systems (ADAS), Electric Vehicles (EVs), Decision Making, Risk Assessments, Communication Networks.
In recent years, the automotive industry has
witnessed a surge in the development of advanced vehicle
technologies that heavily rely on complex algorithms for
decision making. These technologies include advanced
driver assistance systems (ADAS), autonomous driving
systems, and connected vehicle systems, among others.
While these technologies have the potential to improve
safety, reduce accidents, and enhance the overall driving
experience, they also pose significant challenges related to
their reliance on complex algorithms. The increasing
dependency on complex algorithms for decision making
in vehicle technology raises several concerns related to
the reliability, transparency, and accountability of these
systems. One of the primary concerns is the potential for
errors or malfunctions in the algorithms that could lead
to accidents, injuries, or fatalities. Another concern is the
lack of transparency in how these algorithms make
decisions, which can make it difficult for users to
understand and trust the technology. To address these
concerns, there is a need for greater collaboration
between industry stakeholders, regulators, and
researchers to develop robust testing and validation
processes for complex algorithms used in vehicle
technology. Additionally, there is a need for greater
transparency and accountability in how these algorithms
make decisions, such as the use of explainable AI
techniques that can provide insights into the decisionmaking process. Overall, the growing dependence on
complex algorithms for decision making in vehicle
technology presents both opportunities and challenges for
the automotive industry. This paper aims to details by
addressing the concerns related to these algorithms,
stakeholders can work towards realizing the full potential
of these technologies while ensuring safety and reliability
for users.
Keywords :
Advance Driver Assist Systems (ADAS), Electric Vehicles (EVs), Decision Making, Risk Assessments, Communication Networks.
