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.

自动驾驶汽车技术快速发展，对道路交通安全产生了重大影响，同时也解决了多种复杂的交通问题。为了确保自动驾驶汽车之间以及自动驾驶汽车与人类驾驶员之间的合作互动，本研究提出了一个多阶段的方法，采用共享和关怀的车对车通信策略来加强协同行为。通过进行一项调查来验证自动驾驶汽车的驾驶性能，并可用于一个混合交通案例研究，重点研究人类驾驶员如何对待与他们同时驾驶在同一条道路上的自动驾驶汽车。研究结果表明，采用深度强化学习，自动驾驶车辆获得了接近人类驾驶性能的驾驶行为。在自动驾驶汽车网络中采用共享和关怀的车对车通信可以提高其驾驶行为，促进更有效的行动计划，并增进自动驾驶汽车之间的合作行为。调查结果显示，无法保证混合交通中的安全性，因为我们无法控制人类驾驶员出于自我的行为，如果他们决定与自动驾驶汽车竞争。因此，本研究倡导加强对自动驾驶汽车在公共道路上安全融入的研究。