Lateral Control Design and String Stability Analysis for Autonomous Vehicle Platoons During Emergency Lane Changes

Keskeiset käsitteet

This research paper proposes a novel lateral control framework for autonomous vehicles (ACVs) in a platoon formation during an Emergency Lane Change (ELC) maneuver, ensuring lateral string stability by leveraging communicated positional data from both the lead and preceding vehicles.

Tiivistelmä

Bibliographic Information:

Somisetty, N., & Darbha, S. (2024). Lateral String Stability in Autonomous & Connected Vehicle Platoons. 2024 IEEE, 1–4. https://doi.org/10.48550/arXiv.2011.03587

Research Objective:

This paper investigates the lateral control of autonomous vehicles within a platoon during an Emergency Lane Change (ELC) maneuver, aiming to achieve lateral string stability by utilizing limited preview information from the lead and preceding vehicles.

Methodology:

The researchers developed a lateral control framework consisting of feedforward and feedback components. The feedforward controller utilizes preview data from the lead and preceding vehicles to construct a target trajectory, while the feedback controller, based on a second-order model for steering actuation dynamics, uses error signals (lateral error, heading error, and yaw rate error) to maintain the vehicle on the desired trajectory. The D-decomposition technique is employed to determine stabilizing feedback gains. Lateral string stability is theoretically proven for a platoon executing a straight-line maneuver. The proposed control scheme is validated through numerical simulations of a double lane-change maneuver.

Key Findings:

• The proposed lateral control scheme, utilizing information from both the lead and preceding vehicles, effectively maintains lateral string stability during ELC maneuvers.
• Lateral errors are effectively minimized, remaining within an acceptable range throughout the maneuver.
• Steering command angles remain within safe limits and do not exhibit a monotonically increasing trend with increasing platoon size, demonstrating the scalability of the proposed approach.

Main Conclusions:

The proposed lateral control framework, leveraging communicated data from both the lead and preceding vehicles, successfully addresses the challenges of lateral stability and string stability in ACV platoons during ELC maneuvers. The theoretical proof and simulation results validate the effectiveness and scalability of the proposed approach, paving the way for safer and more efficient autonomous vehicle platooning.

Significance:

This research significantly contributes to the field of autonomous vehicle platooning by proposing a novel lateral control scheme that ensures both individual vehicle stability and string stability during critical ELC maneuvers. The findings have practical implications for developing reliable and safe autonomous driving technologies.

Limitations and Future Research:

The study primarily focuses on a double lane-change scenario. Future research could explore the performance of the proposed control scheme under various ELC scenarios, including more complex maneuvers and challenging road conditions. Additionally, incorporating communication delays and uncertainties in positional data would enhance the robustness and practicality of the proposed approach.

Tilastot

The damping ratio (ζ) and natural frequency (ωn) for steering actuation are specified as 0.4056 and 21.4813 rad/s, respectively.
The stabilizing feedback gains were computed across speeds of 10, 20, 30, 40, 50, 60, and 67 mph.
The controller gain vector (ke, kθ, kω) used is (0.06, 0.96, 0.08).
The maximum lateral error observed during the double lane change maneuver is approximately 8 cm.

Lainaukset

"This study addresses the challenge of controlling the lateral dynamics of an ACV in a platoon with limited preview information from the lead and preceding vehicles."
"Connectivity is crucial for executing the proposed lateral control framework, as it enables each ACV in the platoon to access the position information of both the lead ACV and its immediate preceding ACV."
"This study provides a theoretical proof establishing the lateral string stability of a platoon utilizing communicated information from both lead and preceding vehicles while executing a straight-line maneuver."

Tärkeimmät oivallukset

Lateral String Stability in Autonomous & Connected Vehicle Platoons

by Neelkamal So... klo arxiv.org 11-13-2024

https://arxiv.org/pdf/2411.07540.pdf

Syvällisempiä Kysymyksiä

How can the proposed lateral control framework be adapted for mixed-autonomy platoons, where not all vehicles are autonomous?

Adapting the proposed lateral control framework for mixed-autonomy platoons, where only some vehicles are autonomous, presents a significant challenge. Here's a breakdown of the key considerations and potential solutions:
Challenges:

Information Asymmetry: In a mixed-autonomy platoon, not all vehicles can send or receive the necessary data for the proposed control scheme. Human-driven vehicles won't be broadcasting their intended trajectory.
Heterogeneous Dynamics: Human drivers and autonomous systems exhibit different reaction times and driving behaviors. This heterogeneity can introduce uncertainties and disturbances that the control system needs to handle.
Safety and Trust: Ensuring safe interactions between autonomous and human-driven vehicles is paramount. The control system must be robust to unpredictable human behavior and potential errors.
Potential Adaptations:

Sensor Fusion and Prediction: Equip autonomous vehicles with robust sensor suites (LiDAR, radar, cameras) to perceive and predict the trajectories of preceding vehicles, including human-driven ones. This would require advanced perception algorithms and predictive models of human driver behavior.
Adaptive Control Strategies: Implement adaptive control schemes that adjust the control parameters (e.g., gains in the feedback controller) based on the detected behavior of surrounding vehicles. This could involve identifying whether a preceding vehicle is human-driven or autonomous and adjusting the control response accordingly.
Communication Protocols for Mixed Environments: Develop communication protocols that allow autonomous vehicles to share limited information with human drivers (e.g., through vehicle-to-everything (V2X) communication). This could involve transmitting warnings or basic maneuver intentions.
Gradual Autonomy Integration: Introduce autonomy gradually into platoons. For instance, start with a small number of autonomous vehicles at the rear, allowing the system to learn and adapt to mixed traffic conditions before increasing the autonomy level.
Key Considerations:

Human-Factor Studies: Extensive human-factor studies are crucial to understand how human drivers react to and interact with autonomous vehicles in a platoon setting.
Fail-Safe Mechanisms: Robust fail-safe mechanisms are essential to ensure safety in case of communication failures, sensor errors, or unpredictable human behavior.

Could relying solely on communicated data from other vehicles in the platoon introduce vulnerabilities to malicious attacks or sensor errors, and how can these be mitigated?

Yes, relying solely on communicated data from other vehicles in a platoon introduces vulnerabilities to both malicious attacks and sensor errors.
Vulnerabilities:

Malicious Attacks: A malicious actor could potentially:

Spoof GPS Data: Send false GPS position data to following vehicles, causing them to deviate from the intended path.
Inject Fake Trajectory Information: Transmit fabricated trajectory data, leading to unsafe maneuvers.
Jam Communication Channels: Disrupt communication between vehicles, preventing the sharing of crucial information.

Sensor Errors:

GPS Inaccuracies: GPS signals can be affected by atmospheric conditions or multipath reflections, leading to inaccurate position information.
Sensor Malfunctions: Onboard sensors like IMUs can experience drift or malfunctions, compromising the accuracy of communicated data.
Mitigation Strategies:

Data Security and Authentication:

Encryption and Authentication: Implement robust encryption and authentication protocols to ensure the integrity and authenticity of communicated data. This would prevent unauthorized access and data manipulation.
Secure Communication Channels: Utilize dedicated short-range communication (DSRC) or cellular-based V2X technologies with built-in security features.

Redundancy and Sensor Fusion:

Multiple Sensor Sources: Equip vehicles with redundant sensor systems (e.g., multiple GPS receivers, IMUs, LiDAR, radar) to cross-validate data and detect inconsistencies.
Sensor Fusion Algorithms: Develop advanced sensor fusion algorithms that combine data from multiple sources, improving accuracy and robustness against individual sensor errors.

Anomaly Detection and Fault Tolerance:

Behavior-Based Anomaly Detection: Implement algorithms that monitor the behavior of surrounding vehicles and detect anomalies that could indicate malicious attacks or sensor errors.
Fault-Tolerant Control: Design the lateral control system to be fault-tolerant, allowing it to maintain stability and safety even in the presence of some sensor or communication errors. This could involve switching to a more conservative control strategy or relying on redundant data sources.
Key Considerations:

Cybersecurity Expertise: Integrating cybersecurity expertise into the design and development of autonomous vehicle systems is crucial.
Regular Security Updates and Testing: Regular security updates and penetration testing are essential to address emerging threats and vulnerabilities.

If we consider the evolution of autonomous vehicle technology towards swarm intelligence, how might the principles of decentralized control and emergent behavior influence the design of future lateral control systems for platooning?

The evolution of autonomous vehicle technology towards swarm intelligence, incorporating principles of decentralized control and emergent behavior, holds significant potential to revolutionize lateral control systems for platooning.
Here's how:
Decentralized Control:

Distributed Decision-Making: Instead of relying on a designated leader vehicle, each vehicle in the swarm would possess the capability to make independent decisions based on local information and interactions with its neighbors. This reduces reliance on a single point of failure and enhances the platoon's resilience.
Local Communication and Coordination: Vehicles would communicate directly with their immediate neighbors, sharing information about their position, speed, and intended maneuvers. This localized communication reduces the bandwidth requirements and latency issues associated with centralized communication.
Adaptive Formation Control: Decentralized control algorithms would enable the platoon to adapt its formation dynamically based on changing road conditions, traffic flow, or obstacles. Vehicles could seamlessly split and merge, optimizing the platoon's overall efficiency and safety.
Emergent Behavior:

Self-Organization and Optimization: Through local interactions and feedback mechanisms, the platoon would exhibit emergent behavior, self-organizing into efficient and stable formations without explicit central coordination. This could lead to optimized lane usage, reduced air drag, and smoother traffic flow.
Collective Intelligence and Learning: The collective experience and data gathered by individual vehicles could be shared and utilized for continuous learning and improvement of the swarm's overall performance. This could involve adapting to new traffic patterns, optimizing routes, or enhancing safety protocols.
Impact on Lateral Control:

Cooperative Lane Changing: Decentralized control and emergent behavior would facilitate more efficient and coordinated lane changes for the entire platoon. Vehicles could anticipate and adapt to each other's movements, creating safe and smooth transitions.
Obstacle Avoidance and Maneuvering: The swarm could collectively sense and react to obstacles, with individual vehicles adjusting their trajectories in a coordinated manner to maintain safety and optimize the platoon's path.
Robustness to Disturbances: The distributed nature of swarm intelligence would make the platoon more robust to disturbances, such as wind gusts or sudden braking of individual vehicles. The system could quickly adapt and maintain stability through local adjustments and communication.
Key Challenges and Considerations:

Algorithm Design and Verification: Developing robust and verifiable decentralized control algorithms for swarm behavior in complex traffic environments is a significant challenge.
Communication Reliability and Security: Ensuring reliable and secure communication between vehicles in a decentralized setting is crucial for safe and effective swarm behavior.
Safety and Predictability: While emergent behavior offers flexibility, it's essential to ensure that the swarm's actions remain predictable and safe for other road users.
The integration of swarm intelligence principles into lateral control systems for platooning has the potential to significantly enhance efficiency, safety, and adaptability. However, addressing the associated challenges requires interdisciplinary research efforts in control theory, communication systems, and artificial intelligence.