The improved neural network algorithm is applied in the obstacle avoidance path planning of driverless vehicles
ID：185
Submission ID：204 View Protection：ATTENDEE
Updated Time：2023-11-20 13:53:27 Hits：374
Poster Presentation
Start Time：Pending （Asia/Shanghai）
Duration：Pending
Session：[No Session] » [No Session Block]
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Abstract
The intelligent transportation system is gradually rising, and the driverless vehicle technology plays an important role in the development of the intelligent transportation system, whose core is the obstacle avoidance path planning. Bardless obstacle avoidance path planning is to explore the effective obstacle avoidance path from the starting point to the end point to achieve the optimal or approximate optimal value. Based to the shortcomings of traditional neural network algorithm based on the combination of neural network and particle swarm algorithm. The neural network is used to determine the relationship between moving point and obstacle; then the particle swarm algorithm is used to search the optimal value and optimize the generated path. Finally, the improved neural network algorithm is simulated on the MATLAB platform to verify that the improved algorithm can generate the global optimal path and realize the dynamic obstacle avoidance.
Keywords
unmanned driving; route exploration; neural network algorithm; particle swarm algorithm
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