We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| SensorsINI/ddd20-utils | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| filter_frame | Ran | SensorsINI/ddd20-utils/export.py pointer only (licence: LGPL-3.0) · get_code("edede826aa00a236") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neuromorphic event cameras are useful for dynamic vision problems under difficult lighting conditions. To enable studies of using event cameras in automobile driving applications, this paper reports a new end-to-end driving dataset called DDD20. The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames. DDD20 is the longest event camera end-to-end driving dataset to date with 51h of DAVIS event+frame camera and vehicle human control data collected from 4000km of highway and urban driving under a variety of lighting conditions. Using DDD20, we report the first study of fusing brightness change events and intensity frame data using a deep learning approach to predict the instantaneous human steering wheel angle. Over all day and night conditions, the explained variance for human steering prediction from a Resnet-32 is significantly better from the fused DVS+APS frames (0.88) than using either DVS (0.67) or APS (0.77) data alone.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2005.08605")
get_code_for_paper("2005.08605")
have("2005.08605")
Connect an agent — have() is free.