CARLA Simulator-Based Evaluation Framework Development of Lane Detection Accuracy Performance Under Sensor Blockage Caused by Heavy Rain for Autonomous Vehicle

Jeon, Hyeonjae; Kim, YaeOhn; Choi, Minyoung; Park, Donggeon; Son, Sungho; Lee, Jungki; Choi, Gyeung Ho; Lim, Yongseob

doi:10.1109/lra.2022.3192632

Cited by 7 publications

(1 citation statement)

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“…investigating the effects of rain on object detection performance in complex scenarios, whereas real rain simulators are better suited to characterizing the effect of a condition on the sensor itself. For example, Jeon et al investigated the effect of heavy rain on lane detection [9] while Hasirlioglu et al used their real rain simulator to look at sensor performance [4]. Many simulators come with prebuilt rain models, which are usually based on particle models, however, the degree to which these models have been validated is often unclear.…”

Section: Related Workmentioning

confidence: 99%

The design and validation of a rain model for a simulated automotive environment

Brophy¹,

Deegan²,

Salado³

et al. 2023

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This paper presents the design of an accurate rain model for the commercially-available Anyverse automotive simulation environment. The model incorporates the physical properties of rain and a process to validate the model against real rain is proposed. Due to the high computational complexity of path tracing through a particle-based model, a second more computationally efficient model is also proposed. For the second model, the rain is modeled using a combination of a particle-based model and an attenuation field. The attenuation field is fine-tuned against the particle-only model to minimize the difference between the models.

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Section: Related Workmentioning

confidence: 99%