14,698 research outputs found
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving
Machine learning can provide efficient solutions to the complex problems
encountered in autonomous driving, but ensuring their safety remains a
challenge. A number of authors have attempted to address this issue, but there
are few publicly-available tools to adequately explore the trade-offs between
functionality, scalability, and safety.
We thus present WiseMove, a software framework to investigate safe deep
reinforcement learning in the context of motion planning for autonomous
driving. WiseMove adopts a modular learning architecture that suits our current
research questions and can be adapted to new technologies and new questions. We
present the details of WiseMove, demonstrate its use on a common traffic
scenario, and describe how we use it in our ongoing safe learning research
Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles
Self-driving vehicles have their own intelligence to drive on open roads.
However, vehicle managers, e.g., government or industrial companies, still need
a way to tell these self-driving vehicles what behaviors are encouraged or
forbidden. Unlike human drivers, current self-driving vehicles cannot
understand the traffic laws, thus rely on the programmers manually writing the
corresponding principles into the driving systems. It would be less efficient
and hard to adapt some temporary traffic laws, especially when the vehicles use
data-driven decision-making algorithms. Besides, current self-driving vehicle
systems rarely take traffic law modification into consideration. This work aims
to design a road traffic law adaptive decision-making method. The
decision-making algorithm is designed based on reinforcement learning, in which
the traffic rules are usually implicitly coded in deep neural networks. The
main idea is to supply the adaptability to traffic laws of self-driving
vehicles by a law-adaptive backup policy. In this work, the natural
language-based traffic laws are first translated into a logical expression by
the Linear Temporal Logic method. Then, the system will try to monitor in
advance whether the self-driving vehicle may break the traffic laws by
designing a long-term RL action space. Finally, a sample-based planning method
will re-plan the trajectory when the vehicle may break the traffic rules. The
method is validated in a Beijing Winter Olympic Lane scenario and an overtaking
case, built in CARLA simulator. The results show that by adopting this method,
the self-driving vehicles can comply with new issued or updated traffic laws
effectively. This method helps self-driving vehicles governed by digital
traffic laws, which is necessary for the wide adoption of autonomous driving
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