Control of Connected and Automated Vehicles: State of the Art and Future Challenges (1804.03757v1)

Abstract: Autonomous driving technology pledges safety, convenience, and energy efficiency. Challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to enhance awareness and enable cooperation. Connected and automated vehicles (CAVs) have the potential to disrupt mobility, extending what is possible with driving automation and connectivity alone. Applications include real-time control and planning with increased awareness, routing with micro-scale traffic information, coordinated platooning using traffic signals information, eco-mobility on demand with guaranteed parking. This paper introduces a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency. We provide an overview of existing algorithms and their mutual interactions, we present promising optimization-based approaches to CAVs control and identify future challenges.

• The paper presents a dual-layer control architecture combining on-board real-time actions with remote predictive planning for optimal vehicle performance.
• It employs advanced model predictive control in powertrain and motion systems, including cooperative adaptive cruise control to enhance safety and energy efficiency.
• The study identifies challenges like communication standardization and computational complexity, guiding future innovations in eco-driving and mobility management.

Control of Connected and Automated Vehicles: State of the Art and Future Challenges

The paper "Control of Connected and Automated Vehicles: State of the Art and Future Challenges" presents a comprehensive survey of the current landscape in the control architectures for Connected and Automated Vehicles (CAVs). The authors meticulously dissect the essential components and planning methodologies pivotal to enhancing the safety and energy efficiency of autonomous driving systems.

Core Components and Architectural Breakdown

The authors propose a detailed control and planning architecture that bifurcates into on-board real-time controls and remote planning and routing layers. The architecture notably enhances CAVs through increased environmental awareness made possible by connectivity and automation. This dual approach underlines the interoperability between immediate vehicle controls and strategic, long-term trajectory planning.

On-Board Real-Time Control and Planning

The paper explores the intricacies of on-board systems which are crucial for ensuring immediate control actions:

1. Powertrain Control: This section covers gear-shifting, engine on/off, and energy management systems across various vehicle types. The paper emphasizes using Model Predictive Control (MPC) in improving energy efficiency by exploiting predictability in vehicle speed and power demand.
2. Motion Control: Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) are explored, highlighting how vehicle-to-vehicle (V2V) communication significantly enhances safety and reduces inter-vehicle gaps without compromising energy efficiency. Lateral control methodologies are also discussed, with emphasis on robustness and passenger comfort.
3. Motion Planning: Here, the authors analyze real-time trajectory planning. The challenge lies in balancing computational complexity against real-time responsiveness. The authors advocate for approaches tailored to cooperative behaviors enabled by V2V and vehicle-to-infrastructure (V2I) communication.

Remote Planning and Routing

The remote planning layer orchestrates strategic decisions to optimize overall trip performance through advanced data analytics:

1. Battery Charge Planning: The paper highlights predictive planning to optimize battery management in hybrid systems, underscoring the need to adapt to route conditions and energy usage forecasts.
2. Eco-Driving and Coordination: This section evaluates trajectory optimization for single and multiple CAVs, stressing importance on minimizing energy consumption while adhering to temporal and spatial constraints.
3. Eco-Routing: The discussion covers optimization of routing algorithms based on real-time traffic data and road conditions, aiming at energy-efficient pathfinding.

Implications and Future Directions

The capabilities presented in the paper project significant implications for both theoretical development and practical deployments:

• Energy Efficiency: The integration of predictive control strategies rooted in detailed environmental awareness underscores potential reductions in energy consumption across various driving environments.
• Safety Improvements: Enhanced control systems, particularly CACC, suggest substantial improvements in roadway safety through refined vehicle coordination.
• Traffic Flow and Grid Balance: The synchronization of CAV trajectories with traffic conditions and grid availability presents opportunities for enhancing urban mobility and alleviating congestion.

Conclusion

The paper serves as an insightful resource into the control frameworks necessary for the successful deployment of CAVs. While significant developments are covered, the paper candidly addresses existing challenges, including communication standardization, real-world validation, and the need for broader experimental applications. The future trajectory likely involves a nuanced blend of advanced algorithmic control, detailed modeling, and continuous integration with rapidly evolving technological landscapes. The continued evolution of this field promises substantial advancements in sustainable transportation and intelligent mobility systems.