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(-0.11,-13.82), (-0.07,-9.70), (-0.04,-5.42)]Mission Goal: FORWARDFront object detections:Front object detected, object type: pedestrian, object id: 4, position: (6.49, 16.88), size: (0.66, 0.72)Future trajectories for specific objects:Object type: pedestrian, object id: 4, future waypoint coordinates in 3s: [(6.46, 17.53), (6.44, 18.20), (6.42, 18.89), (6.38, 19.57), (6.37, 20.26), (6.34, 20.91)]Distance to both sides of road shoulders of selected locations:Location (6.49, 16.88) distance to left shoulder is 2.5m and distance to right shoulder is uncertain## Expected Output:*****Chain-of-Thoughts Reasoning:***** - Notable Objects: car at (2.44,44.97) Potential Effects: within the safe zone of the ego-vehicle at 2.5 second*****Task Planning:*****Behavior: MOVE FORWARD, Speed: A DECELERATIONDriving plan: MOVE FORWARD WITH A DECELERATION Figure 8. An example of motion planning. | ALanguageAgentforAutonomousDriving |
These trends continued with Deep Speech 2 (Amodei et al.,
2015) being a notable system developing high-throughput
distributed training across 16 GPUs and scaling to 12,000
hours of training data while demonstrating continuing im-
provements at that scale. By leveraging semi-supervised
pre-training, Narayanan et al. (2018) were able to grow
dataset size much further and study training on 162,000
hours of labeled audio. More recent work has explored | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |