Development and Assessment of Autonomous Vehicles in Both Fully
Automated and Mixed Traffic Conditions
- URL: http://arxiv.org/abs/2312.04805v1
- Date: Fri, 8 Dec 2023 02:40:11 GMT
- Title: Development and Assessment of Autonomous Vehicles in Both Fully
Automated and Mixed Traffic Conditions
- Authors: Ahmed Abdelrahman
- Abstract summary: The paper presents a multi-stage approach, starting with the development of a single AV and progressing to connected AVs.
A survey is conducted to validate the driving performance of the AV and will be utilized for a mixed traffic case study.
Results show that using deep reinforcement learning, the AV acquired driving behavior that reached human driving performance.
The adoption of sharing and caring based V2V communication within AV networks enhances their driving behavior, aids in more effective action planning, and promotes collaborative behavior amongst the AVs.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Autonomous Vehicle (AV) technology is advancing rapidly, promising a
significant shift in road transportation safety and potentially resolving
various complex transportation issues. With the increasing deployment of AVs by
various companies, questions emerge about how AVs interact with each other and
with human drivers, especially when AVs are prevalent on the roads. Ensuring
cooperative interaction between AVs and between AVs and human drivers is
critical, though there are concerns about possible negative competitive
behaviors. This paper presents a multi-stage approach, starting with the
development of a single AV and progressing to connected AVs, incorporating
sharing and caring V2V communication strategy to enhance mutual coordination. A
survey is conducted to validate the driving performance of the AV and will be
utilized for a mixed traffic case study, which focuses on how the human drivers
will react to the AV driving alongside them on the same road. Results show that
using deep reinforcement learning, the AV acquired driving behavior that
reached human driving performance. The adoption of sharing and caring based V2V
communication within AV networks enhances their driving behavior, aids in more
effective action planning, and promotes collaborative behavior amongst the AVs.
The survey shows that safety in mixed traffic cannot be guaranteed, as we
cannot control human ego-driven actions if they decide to compete with AV.
Consequently, this paper advocates for enhanced research into the safe
incorporation of AVs on public roads.
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