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LiDAR-as-Camera for End-to-End Driving.

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  • 1Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia.

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LiDAR images from Ouster sensors, perfectly aligned in space and time, are sufficient for autonomous driving. These depth-sensing inputs perform as well as cameras and offer better weather resilience and generalization for self-driving neural networks.

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Autonomous driving systems rely on transforming sensory data into driving commands.
  • End-to-end driving models commonly use camera inputs, but depth sensing can improve performance.
  • Integrating depth and visual data from separate sensors presents alignment challenges.

Purpose of the Study:

  • To investigate the utility of perfectly aligned, multi-channel LiDAR images as input for self-driving neural networks.
  • To evaluate the performance of LiDAR-based end-to-end driving models compared to camera-based models.
  • To explore the impact of LiDAR data on robustness to weather conditions and generalization capabilities.

Main Methods:

  • Utilized surround-view LiDAR images (depth, intensity, ambient radiation) from Ouster sensors, inherently aligned in space and time.
Keywords:
LiDAR in autonomous drivingautonomous drivingend-to-end drivingevaluationgeneralization

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  • Trained and evaluated neural network models for road-following tasks using LiDAR images as input.
  • Compared LiDAR-based model performance against traditional camera-based models.
  • Analyzed the correlation between temporal smoothness of off-policy predictions and on-policy driving ability.

    • Main Results:

    • LiDAR images proved sufficient for real-car road-following, achieving performance comparable to camera-based models.
    • LiDAR-based models demonstrated reduced sensitivity to adverse weather conditions.
    • Models utilizing LiDAR input exhibited enhanced generalization capabilities.
    • Temporal smoothness of predictions was found to be a strong correlate of driving performance, similar to mean absolute error.

    Conclusions:

    • Multi-channel, aligned LiDAR images are a viable and effective input for end-to-end autonomous driving systems.
    • LiDAR-based approaches offer advantages in robustness and generalization over traditional camera-only systems.
    • Temporal smoothness is a valuable metric for evaluating the driving capability of predictive models.