Leading Vehicle Length Estimation Using Pressure Data for Use in Autonomous Driving
Name
Matis Ottan
Abstract
Overtaking vehicles is a risky manoeuvre for human drivers and an even more difficult challenge for autonomous cars. The algorithms for overtaking require extensive information about the surrounding environment including knowing the length of a leading vehicle. The usual sensing modalities used in autonomous vehicles (vision, radar, LiDAR) are not suitable for estimating that length. In literature, flow sensing has been shown to aid underwater robots in navigation and localization. This suggests that flow sensing could also provide useful information for autonomous vehicles. This study investigates air flow data behind truck-sized bluff bodies using data acquired from Computational Fluid Dynamics (CFD) simulations. The proposed features for classification are based on Fast-Fourier transforms. The results show that pressure data can be used to differentiate between various truck lengths, indicating that flow sensors could aid autonomous vehicles in overtaking.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Naveed Muhammad
Defence year
2022