PINN-Driven Autonomous Driving: Applications of Physics-Informed Neural Networks in Trajectory Prediction and Stability Control

The project is named Physics-Informed-ESP-control, released by EhsanJavahersaz1 on GitHub on June 7, 2026 (under MIT License). Its core is to use Physics-Informed Neural Networks (PINNs) to integrate physical constraints with deep learning, solving trajectory prediction and stability control problems in autonomous driving, and enhancing system reliability and interpretability.

Autonomous driving has extremely high requirements for control systems. Traditional physical models struggle to capture complex nonlinearities; pure data-driven methods lack physical interpretability and are prone to anomalies under boundary conditions. PINNs integrate the advantages of both by embedding physical constraints during training—retaining data-driven capabilities while ensuring results comply with physical laws.

The core of PINNs is integrating physical equations as a regularization term into the loss function: L_total = L_data + λ*L_physics (λ is a balancing hyperparameter). The ESP control system stabilizes the vehicle through state monitoring (yaw rate, lateral acceleration, etc.), stability judgment, and intervention control (braking individual wheels). PINNs learn accurate dynamic models, predict control effects, and optimize parameter adjustments in this process.

PINNs have five major advantages over traditional methods: 1. High data efficiency (physical constraints reduce reliance on annotations); 2. Strong generalization ability (universality of physical laws); 3. Good interpretability (constrained by physical laws); 4. High safety (avoids abnormal outputs that violate physics); 5. Naturally satisfies boundary conditions. Technical comparison:

Method	Advantages	Limitations
Pure physical model	Strong interpretability, compliant with physics	Difficult to model complex nonlinearities
Pure data-driven	Strong expressive power, end-to-end learning	Data-dependent, lacks physical constraints
PINN	Integrates advantages of both	High computational cost, complex implementation

Implementing PINNs faces challenges: high computational complexity (high-order derivative calculation), difficulty in hyperparameter tuning (λ weight), need for accurate physical modeling (error propagation), and high real-time requirements (millisecond-level response). Industry significance: meets the needs of safety-critical systems in autonomous driving, complies with regulatory requirements for interpretability, and serves as a technical foundation for hybrid intelligent architectures.

This project demonstrates the application potential of PINNs in autonomous driving. In the future, PINNs and their variants are expected to be applied in scenarios such as path planning, decision control, and sensor fusion. For developers focusing on the integration of AI and physics, this is a direction worth exploring in depth.