Optimal Sequencing and Motion Control in a Roundabout with Safety Guarantees

Centrala begrepp

Developing a decentralized MPC-CLBF framework for optimal sequencing and motion control in roundabouts improves travel time, energy consumption, and safety for CAVs.

Sammanfattning

The paper introduces a controller for Connected and Automated Vehicles (CAVs) navigating single-lane roundabouts. It optimizes sequencing and motion control to minimize travel time, energy consumption, and ensure safety. The integration of Model Predictive Control (MPC) with Control Lyapunov-Barrier Functions (CLBFs) addresses control issues in interconnected control zones. Simulations demonstrate the effectiveness of the proposed controller under varying traffic demands. Research focuses on improving traffic flow efficiency while reducing fuel consumption through precise trajectory design.

Statistik

Total objective value under MPC-CLBF with H = 20 shows 71.0% improvement compared to the SUMO baseline.
Total energy consumption reduced by 94.1% using MPC-CLBF compared to SUMO.
Rear-end conflicts reduced by over 95% using MPC-CLBF compared to other methods.

Citat

"The emergence of Connected and Automated Vehicles (CAVs) along with real-time communication between mobile endpoints and the infrastructure make it possible to achieve smoother traffic flow."
"Model Predictive Control (MPC) is used to account for future performance across different CZs, achieving optimality over a tunable receding horizon."
"The MPC-CLBF method outperforms OCBF since it can predict future conflicts and preempt them with smoother control actions."

Viktiga insikter från

Optimal Sequencing and Motion Control in a Roundabout with Safety Guarantees

by Yingqing Che... på arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.09923.pdf

Djupare frågor

How can the decentralized MPC-CLBF framework be adapted for mixed-traffic scenarios involving human-driven vehicles

In mixed-traffic scenarios involving both Connected and Automated Vehicles (CAVs) and human-driven vehicles, the decentralized MPC-CLBF framework can be adapted by incorporating specific strategies to ensure safe interaction between the two types of vehicles.

Behavior Prediction: The system can utilize advanced sensors and communication technologies to predict the behavior of human drivers based on their movements, speed patterns, and signaling cues. This predictive capability allows CAVs to anticipate potential conflicts or unexpected actions from human drivers.

Adaptive Control Strategies: The MPC-CLBF controller can adjust its control algorithms dynamically based on the presence of human-driven vehicles in proximity. By integrating machine learning models that analyze historical data on human driver behavior, the system can adapt its decision-making process to accommodate different driving styles.

Communication Protocols: Implementing standardized communication protocols between CAVs and human-driven vehicles enables efficient information exchange regarding intentions, maneuvers, and traffic conditions. This real-time communication enhances coordination and safety in mixed-traffic environments.

Safety Assurance Mechanisms: Specific safety measures such as collision avoidance systems, emergency braking capabilities, and enhanced situational awareness modules can be integrated into the MPC-CLBF framework to mitigate risks associated with interactions between CAVs and traditional vehicles.

Regulatory Compliance: Ensuring compliance with existing traffic regulations while considering the unique characteristics of autonomous technology is crucial for seamless integration into mixed-traffic settings.

What are the potential challenges in implementing the MPC-CLBF controller in real-world roundabout settings

Implementing the MPC-CLBF controller in real-world roundabout settings may face several challenges that need to be addressed for successful deployment:

Hardware Infrastructure: Adequate infrastructure support such as high-quality sensors, reliable communication networks, robust computing systems onboard each vehicle or at roadside units is essential for real-time data processing and decision-making.

Algorithm Complexity: The computational complexity involved in solving optimization problems within a short time frame requires efficient algorithms capable of handling large datasets while ensuring rapid response times.

Integration with Existing Systems: Seamless integration with existing traffic management systems, road signage recognition mechanisms, pedestrian detection technologies is critical for cohesive operation within urban environments.

Validation & Testing: Rigorous testing procedures under various scenarios including adverse weather conditions, heavy traffic volumes are necessary to validate the effectiveness and reliability of the controller before widespread implementation.

5Legal & Ethical Considerations: Addressing legal frameworks related to liability issues arising from accidents involving autonomous vehicles is crucial along with ethical considerations surrounding decision-making during unforeseen circumstances.

How might advancements in autonomous vehicle technology impact urban congestion levels in the future

Advancements in autonomous vehicle technology have significant implications for future urban congestion levels:
1Traffic Flow Optimization: Autonomous vehicles equipped with advanced AI algorithms can optimize traffic flow by reducing bottlenecks through coordinated merging at intersections like roundabouts leading to smoother transitions without sudden stops or accelerations which contribute significantly towards congestion reduction.
2Efficient Routing: Smart routing algorithms integrated into autonomous vehicle systems enable dynamic route planning based on real-time traffic data resulting in distributed vehicular distribution across multiple routes rather than congesting specific roads or junctions.
3Reduced Accidents & Traffic Incidents: Autonomous vehicles' superior sensing capabilities coupled with quick reaction times minimize accidents caused by human error thereby preventing road closures due to collisions which often lead to congestion build-up.
4Shared Mobility Services: Implementation of shared mobility services using fleets of autonomous cars reduces overall vehicle ownership leading fewer cars on roads hence decreasing congestion levels especially during peak hours
5Infrastructure Adaptation: Urban planners may redesign city layouts considering increased adoption of self-driving cars potentially creating dedicated lanes or zones optimized specifically for these vehicles further enhancing efficiency throughout metropolitan areas
These advancements collectively contribute towards a future where urban congestion levels could see substantial reductions paving way for more sustainable transportation ecosystems benefiting both commuters as well as environmental conservation efforts