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In dense and dynamic scenarios, planning a safe and comfortable trajectory isfull of challenges when traffic participants are driving at high speed. Theclassic graph search and sampling methods first perform path planning and thenconfigure the corresponding speed, which lacks a strategy to deal with thehigh-speed obstacles. Decoupling optimization methods perform motion planningin the S-L and S-T domains respectively. These methods require a large freeconfiguration space to plan the lane change trajectory. In dense dynamicscenes, it is easy to cause the failure of trajectory planning and be cut in byothers, causing slow driving speed and bring safety hazards. We analyze thecollision relationship in the spatio-temporal domain, and propose aninstantaneous analysis model which only analyzes the collision relationship atthe same time. In the model, the collision-free constraints in 3Dspatio-temporal domain is projected to the 2D space domain to remove redundantconstraints and reduce computational complexity. Experimental results show thatour method can plan a safe and comfortable lane-changing trajectory in densedynamic scenarios. At the same time, it improves traffic efficiency andincreases ride comfort.

IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways