Lane Change Trajectory Planning for Personalized Driving Comfort and Mobility Efficiency
Lane changing entails simultaneous longitudinal and lateral motions that affect driving comfort and mobility efficiency. Because these motions are tightly coupled and subject to substantial inter-vehicle variability, trajectory planning for lane-change maneuvers is characterized by a highly personalized nature. This study proposes a neural network-driven planner that integrates a third-order polynomial trajectory generator with a learning module that infers optimal trajectory parameters across diverse driving conditions. Using a shared backbone with dual heads, one head ensures all-condition operational guarantees, while the other captures driver-specific preferences for comfort or mobili...
| Authors | Haoxuan Dong, Dongjun Li, Ziyou Song |
|---|---|
| Primary category | cs.RO |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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