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Member:sungbeanJo_paper [2021/03/04 18:00] sungbean |
Member:sungbeanJo_paper [2021/04/21 22:08] (current) sungbean |
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| - | To cope with the | + | get_config_param active timestamp_mode |
| - | inertia problem without an explicit mapping of potential | + | TIME_FROM_INTERNAL_OSC |
| - | causes or on-policy interventions, we jointly train a sensorimotor | + | get_config_param active multipurpose_io_mode |
| - | controller with a network that predicts the ego vehicle’s | + | OUTPUT_OFF |
| - | speed. Both neural networks share the same representation | + | get_config_param active sync_pulse_in_polarity |
| - | via our ResNet perception backbone. Intuitively, | + | ACTIVE_LOW |
| - | what happens is that this joint optimization enforces the | + | get_config_param active nmea_in_polarity |
| - | perception module to have speed related features into the | + | ACTIVE_HIGH |
| - | learned representation. This reduces the dependency on input | + | get_config_param active nmea_baud_rate |
| - | speed as the only way to get dynamics of the scene, | + | BAUD_9600 |
| - | leveraging instead visual cues that are predictive of the car’s | + | |
| - | velocity (e.g., free space, curves, traffic light states, etc). | + | |
