Lane Change Trajectory Planning for Personalized Driving Comfort and Mobility Efficiency

Researchers propose a neural network-based lane-change trajectory planner that uses dual-head architecture to balance safety guarantees with personalized driving preferences. The system adaptively switches between a baseline safe mode and a driver-specific comfort/efficiency mode based on contextual driving conditions, enabling autonomous vehicles to optimize maneuvers while maintaining feasibility across diverse scenarios.

This research addresses a critical challenge in autonomous vehicle development: reconciling the need for universally safe driving behavior with the reality that human preferences for comfort and efficiency vary significantly. The proposed neural network architecture tackles the lane-change problem—one of the most complex maneuvers in autonomous driving—by simultaneously handling longitudinal and lateral motion planning, which are inherently coupled and difficult to optimize independently.

The innovation lies in the dual-head switching mechanism, which represents a pragmatic engineering solution to a fundamental tension in autonomous systems. Rather than forcing a single optimization criterion, the architecture maintains a baseline planner that guarantees safe operation under all conditions while allowing a secondary personalized module to enhance comfort and efficiency when sufficient driver-preference data exists. This approach mirrors real-world deployment constraints where systems must operate safely even with incomplete information.

For the autonomous vehicle industry, this development demonstrates progress toward more nuanced decision-making in safety-critical systems. The statistical gating mechanism using logistic regression enables context-aware transitions between operational modes, suggesting that future AVs can become more adaptive without sacrificing reliability. The Monte Carlo validation approach provides confidence that the system handles edge cases effectively.

The implications extend beyond lane-changing to broader trajectory planning challenges in autonomous mobility. As manufacturers balance regulatory requirements for provable safety with consumer demands for natural, efficient driving experiences, this type of hybrid architecture becomes increasingly valuable. The research validates that personalization and safety are not mutually exclusive when properly architected.

• →Dual-head neural network architecture balances universal safety guarantees with personalized driving preferences
• →Third-order polynomial trajectory generator enables smooth simultaneous longitudinal and lateral motion control
• →Statistical gating mechanism adaptively selects operational mode based on driving conditions and data availability
• →Monte Carlo simulations validate feasibility across diverse scenarios with insufficient personalized training data
• →Approach enables autonomous vehicles to optimize comfort and efficiency without compromising safety guarantees