Decentralized Cooperative Driving with Single-Agent Actor Critic Model

Alapfogalmak

Introducing a novel asymmetric actor-critic model for decentralized cooperative driving policies using single-agent reinforcement learning.

Kivonat

Active traffic management incorporating autonomous vehicles (AVs) aims to improve traffic flow. Challenges like continuous traffic flow and partial observability are addressed through a novel asymmetric actor-critic model. Extensive evaluations show potential in enhancing traffic flow at bottleneck locations. Conservative AV driving behaviors are explored, with the proposed cooperative policy mitigating potential traffic slowdowns while ensuring safety. Decentralized decision-making based on local observations of AVs is crucial for effective traffic management.

Statisztikák

"Our approach employs attention neural networks with masking."
"The experiment results illustrate that our proposed cooperative policy can mitigate potential traffic slowdowns without compromising safety."
"The findings highlight the capacity of our method to substantially enhance traffic flow using decentralized policies and partial observations."
"The state feature consists of a 2D vector representing vehicle features."
"The observation space accommodates partial observability by limiting each AV to acquiring features from nearby vehicles within its sensing range."
"The joint action space has a dimension of N × da, where da is the number of discrete actions of each vehicle."
"A multi-categorical distribution π(a | s) is built for the actions based on the logits output by the linear projection."

Idézetek

"Our model shows great potential for improving traffic flow at diverse bottleneck locations within the road system."
"The experiment results illustrate that our proposed cooperative policy can mitigate potential traffic slowdowns without compromising safety."
"Our approach utilizes attention neural networks with masking to manage varying traffic input and effectively deal with partial observability."

Főbb Kivonatok

Single-Agent Actor Critic for Decentralized Cooperative Driving

by Shen... : arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11914.pdf

Mélyebb kérdések

How can decentralized control methods be optimized to handle dynamic real-world traffic scenes more effectively

Decentralized control methods can be optimized to handle dynamic real-world traffic scenes more effectively through several key strategies. Firstly, incorporating advanced reinforcement learning techniques, such as multi-agent reinforcement learning (MARL), can enable individual autonomous vehicles (AVs) to adapt and cooperate in real-time based on local observations. By allowing AVs to learn decentralized cooperative driving policies through single-agent reinforcement learning, they can dynamically adjust their behavior to optimize traffic flow at bottlenecks.
Furthermore, attention mechanisms and masking techniques can be utilized within the actor-critic model to manage variable traffic inputs and partial observability effectively. These mechanisms help the AVs focus on relevant information from nearby vehicles within their sensing range while reducing computational complexity.
Moreover, parameter sharing among policies for different AVs can streamline the training process by reducing the exploration space during training. This approach enhances policy effectiveness by focusing on AVs that have a significant impact on traffic flow rather than all vehicles in the environment.
In summary, optimizing decentralized control methods for dynamic real-world traffic scenes involves leveraging MARL approaches, attention mechanisms with masking techniques, and parameter sharing among policies to enhance cooperation and adaptability of individual AVs.

What ethical considerations should be taken into account when designing algorithms that balance safety and efficiency in autonomous driving

When designing algorithms that balance safety and efficiency in autonomous driving, several ethical considerations must be taken into account to ensure responsible deployment of these technologies. One crucial consideration is determining how much risk an algorithm should take when making decisions that involve trade-offs between safety and efficiency. Algorithms need clear guidelines on prioritizing human safety over other objectives without compromising overall system performance.
Additionally, transparency in algorithm decision-making is essential for accountability and trust-building with users and stakeholders. Ensuring that algorithms are interpretable allows regulators and users to understand why certain decisions were made in critical situations where safety is paramount.
Moreover, addressing biases in data used for training these algorithms is vital to prevent discriminatory outcomes or unfair treatment towards specific groups or individuals. Ethical AI principles like fairness, accountability, transparency, privacy preservation should guide the design process of algorithms balancing safety with efficiency in autonomous driving systems.

How can advancements in reinforcement learning contribute to addressing challenges in urban transportation systems beyond traditional approaches

Advancements in reinforcement learning offer significant potential for addressing challenges beyond traditional approaches in urban transportation systems. By leveraging deep reinforcement learning models like asymmetric actor-critic structures trained using single-agent RL algorithms specifically designed for decentralized control scenarios involving autonomous vehicles (AVs), it becomes possible to optimize traffic management strategies dynamically based on real-time conditions.
These advancements allow for more adaptive responses to changing environments compared to static rule-based systems or centralized controllers limited by communication bandwidth constraints or adverse weather conditions common in traditional approaches.
Furthermore, integrating attention neural networks with masking techniques enables efficient handling of continuous traffic dynamics while ensuring partial observability remains manageable even under complex scenarios like intersections or lane drops where multiple factors influence vehicle interactions simultaneously.
Overall advancements in RL not only improve operational efficiencies but also pave the way for safer transportation systems by enabling cooperative behaviors among diverse road users including both human-driven vehicles (HVs) and AVs through sophisticated decision-making processes guided by learned policies tailored towards enhancing overall system performance while maintaining high standards of safety.