What are the challenges and opportunities of using lidar for AVs in rainy weather?

2 Rain scattering

Another problem that lidar faces in rainy weather is rain scattering, which means that the laser pulses are deflected and dispersed by the raindrops. This can create multiple reflections and echoes that confuse the lidar system and reduce its accuracy and reliability. Rain scattering can also interfere with the detection of other objects and obstacles, such as pedestrians, vehicles, or signs, that are behind or within the rain. To cope with this issue, lidar systems can use advanced algorithms and filters to identify and remove the rain signals from the data. They can also use polarization techniques, which exploit the difference in the orientation of the electric field of the laser beam and the raindrops, to distinguish between them.

3 Sensor fusion

One of the solutions that AV developers use to overcome the challenges of lidar in rainy weather is sensor fusion, which means combining the data from different types of sensors, such as cameras, radar, ultrasound, or GPS, to create a more comprehensive and robust representation of the environment. Sensor fusion can help compensate for the weaknesses and limitations of each sensor, and enhance the accuracy and reliability of the perception and decision-making processes. For example, radar can provide better range and penetration in rain than lidar, while cameras can provide better color and texture information. By fusing the data from lidar, radar, and cameras, an AV can achieve a more complete and accurate understanding of its surroundings in wet conditions.

4 Data augmentation

Another solution that AV developers use to overcome the challenges of lidar in rainy weather is data augmentation, which means modifying or generating synthetic data to increase the diversity and quality of the training and testing datasets for the AV models. Data augmentation can help improve the performance and generalization of the AV models, and reduce the overfitting and underfitting problems. For example, data augmentation can simulate different levels and types of rain, such as drizzle, shower, or storm, and apply them to the lidar data to create more realistic and challenging scenarios for the AV models to learn from. Data augmentation can also use generative adversarial networks (GANs), which are a type of neural network that can create realistic and high-quality synthetic data, to generate new lidar data with rain effects.

Lidar is a powerful and versatile technology for AVs, but it also has some limitations and challenges in rainy weather. By using sensor fusion and data augmentation, AV developers can enhance the performance and robustness of lidar, and enable AVs to adapt to changing weather and road conditions.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?