Safe and Stable Connected Cruise Control for Connected Automated Vehicles with Response Lag

核心概念

Connected automated vehicles (CAVs) can leverage vehicle-to-everything (V2X) connectivity to improve fuel economy and traffic efficiency through connected cruise control (CCC). However, the safety of CCC must be guaranteed, especially in the presence of response lag. This paper analyzes the stability and safety of CCC, and proposes a safety-critical CCC controller that ensures safety while preserving the benefits of CCC.

摘要

This paper investigates the safety and stability of connected cruise control (CCC) for connected automated vehicles (CAVs) in the presence of response lag.

The authors first conduct a stability analysis to derive conditions for plant stability and head-to-tail string stability of the CCC system. They show that in most cases, the CCC system needs to be both plant stable and head-to-tail string stable to achieve safety.

Next, the authors analyze the safety of the nominal CCC design using control barrier function (CBF) theory. They derive safe regions in the parameter space of the CCC gains, and show that the response lag has a detrimental effect on safety, leading to a critical lag value above which safe CCC gains do not exist.

To overcome the limitations of the nominal CCC, the authors propose a safety-critical CCC controller that leverages CBFs to minimally modify the nominal CCC when necessary to ensure safety. This allows the CAV to benefit from the high performance of CCC while guaranteeing safety, even in the presence of response lag and with connectivity to multiple vehicles ahead.

The proposed safety-critical CCC is evaluated through numerical simulations using real traffic data. The results demonstrate that the safety-critical CCC can leverage connectivity to improve safety, and it guarantees safety regardless of the lag and connectivity architecture while preserving the high performance of CCC.

統計資料

The CAV has a speed limit of 30 m/s and a standstill distance of 5 m.
The human-driven vehicles (HVs) have a delay of 0.9 s and a range policy gradient of 0.6 1/s.
The safe gains for the CAV are A = 0.6 1/s, B1 = 0.53 1/s, and Bn+1 = 0.03 1/s.
The unsafe gains for the CAV are A = 0.6 1/s, B1 = 0.53 1/s, and Bn+1 = 0.5 1/s.
The safe distance for the CAV is 1 m, and the inverse time headway is 0.6 1/s.
The speed difference limit is 15 m/s.
The CBF parameters are γ = 1 1/s and γe = 1 1/s.
The speed perturbation for the connected human-driven vehicle (CHV) is 15 m/s.

引述

"To guarantee safety even with lag while preserving the benefits of CCC, we synthesize safety-critical CCC using CBFs."
"We determine the critical value of this lag, above which safe CCC gains do not exist."
"With the proposed safety-critical CCC, the CAV can leverage information from connected vehicles farther ahead to improve its safety."

從以下內容提煉的關鍵洞見

Safe and Stable Connected Cruise Control for Connected Automated Vehicles with Response Lag

by Yuchen Chen,... 於 arxiv.org 09-12-2024

https://arxiv.org/pdf/2409.06884.pdf

深入探究

How can the proposed safety-critical CCC be extended to handle input constraints and other safety criteria beyond the time headway?

The proposed safety-critical Connected Cruise Control (CCC) can be extended to handle input constraints and additional safety criteria by integrating control barrier functions (CBFs) that account for these factors. Input constraints, such as maximum acceleration and deceleration limits, can be incorporated into the safety-critical CCC framework by modifying the quadratic programming problem used to derive the safety filter. This involves adding constraints that ensure the control input remains within specified bounds, thereby preventing unsafe maneuvers that could lead to collisions or loss of control.
To address other safety criteria beyond time headway, such as maintaining a safe distance from obstacles or ensuring safe lateral movements during lane changes, the CBFs can be designed to encapsulate these criteria. For instance, a CBF could be defined to ensure that the distance to the nearest obstacle remains above a certain threshold, or that the vehicle's lateral position remains within safe limits during maneuvers. By formulating these criteria as additional constraints in the safety filter, the CCC can dynamically adjust its control inputs to maintain safety while still optimizing for performance.
Moreover, the integration of multi-objective optimization techniques can be employed to balance various safety criteria, allowing the CCC to prioritize different safety aspects based on the driving context. This holistic approach ensures that the safety-critical CCC remains robust and adaptable to a wide range of driving scenarios, enhancing the overall safety of connected automated vehicles (CAVs).

What are the potential trade-offs between safety, stability, and performance in the design of CCC controllers, and how can they be balanced?

In the design of CCC controllers, there are inherent trade-offs between safety, stability, and performance. Safety is paramount, as it ensures that the vehicle operates within safe parameters to prevent accidents. However, stringent safety measures can lead to conservative control strategies that may compromise performance, such as slower response times or reduced efficiency in maintaining desired speeds.
Stability is another critical aspect, as it ensures that the vehicle can respond predictably to changes in the environment and maintain a smooth trajectory. However, achieving stability often requires conservative control gains, which can limit the vehicle's ability to respond quickly to dynamic traffic conditions, thereby affecting performance.
To balance these trade-offs, designers can employ several strategies:

Adaptive Control Strategies: Implementing adaptive control techniques that adjust control gains based on real-time traffic conditions can help maintain performance while ensuring safety and stability. This allows the CCC to be responsive to varying scenarios without compromising safety.

Safety Filters: Utilizing safety filters, as proposed in the safety-critical CCC framework, allows for the optimization of performance under normal conditions while activating safety measures only when necessary. This minimizes the impact on performance during safe driving conditions while ensuring safety during critical situations.

Multi-Objective Optimization: Employing multi-objective optimization techniques can help identify the optimal balance between safety, stability, and performance. By defining a cost function that incorporates all three aspects, designers can systematically explore the trade-offs and select control parameters that achieve a desirable balance.

Simulation and Testing: Extensive simulation and real-world testing can provide insights into how different control strategies perform under various conditions. This empirical data can inform adjustments to the CCC design, ensuring that safety, stability, and performance are adequately balanced.

By carefully considering these trade-offs and employing adaptive strategies, designers can create CCC controllers that effectively enhance the safety and performance of connected automated vehicles.

How can the safety-critical CCC framework be applied to other connected and automated vehicle applications, such as lane changing, merging, or intersection crossing?

The safety-critical CCC framework can be effectively applied to various connected and automated vehicle applications, including lane changing, merging, and intersection crossing, by leveraging the principles of control barrier functions (CBFs) and vehicle-to-everything (V2X) communication.

Lane Changing: In lane changing scenarios, the safety-critical CCC can utilize CBFs to ensure that the vehicle maintains a safe distance from adjacent vehicles while executing the maneuver. By incorporating information from connected vehicles in adjacent lanes, the CCC can assess the safety of the lane change in real-time and adjust its trajectory accordingly. The safety filter can intervene if the vehicle's intended path poses a risk of collision, ensuring that the lane change is executed safely.

Merging: During merging maneuvers, the safety-critical CCC can be designed to account for the dynamics of both the merging vehicle and the vehicles already in the target lane. By using CBFs to define safe merging criteria, such as maintaining a minimum gap to the vehicle ahead, the CCC can optimize its speed and acceleration to facilitate a smooth and safe merge. The framework can also adapt to varying traffic conditions by adjusting control inputs based on the behavior of surrounding vehicles.

Intersection Crossing: For intersection crossing, the safety-critical CCC can integrate real-time data from connected traffic signals and other vehicles to assess the safety of crossing. CBFs can be employed to ensure that the vehicle does not enter the intersection if it detects potential conflicts with other vehicles or pedestrians. The safety filter can dynamically adjust the vehicle's speed and trajectory to avoid unsafe situations, such as running a red light or colliding with a crossing pedestrian.

Multi-Vehicle Coordination: In scenarios involving multiple connected vehicles, the safety-critical CCC can facilitate coordinated maneuvers, such as platooning or cooperative lane changes. By sharing information about their states and intentions, vehicles can optimize their movements to enhance safety and efficiency. The safety-critical framework ensures that even in complex interactions, safety is prioritized while allowing for performance optimization.

By applying the safety-critical CCC framework to these diverse applications, connected and automated vehicles can enhance their operational safety and efficiency, ultimately contributing to safer and more efficient roadways.