What are the best LiDAR-based obstacle avoidance practices for autonomous vehicles?

1 Sensor fusion

One of the best practices for LiDAR-based obstacle avoidance is to combine it with other sensors, such as cameras, radars, and ultrasonic sensors. This is called sensor fusion, and it can improve the accuracy, reliability, and robustness of the obstacle detection system. Sensor fusion can also compensate for the weaknesses of each sensor, such as the low resolution of LiDAR, the limited range of cameras, or the interference of radars. By integrating multiple sensor inputs, the AV can get a more comprehensive and reliable picture of the surrounding environment and potential hazards.

2 Data processing

Another best practice for LiDAR-based obstacle avoidance is to optimize the data processing pipeline, which involves filtering, segmentation, clustering, and classification of the LiDAR data. Filtering is the process of removing noise and outliers from the raw LiDAR data, such as reflections, raindrops, or dust. Segmentation is the process of dividing the filtered data into regions of interest, such as road surfaces, lanes, or objects. Clustering is the process of grouping the segmented data into clusters based on proximity or similarity, such as cars, pedestrians, or trees. Classification is the process of assigning labels to the clusters based on their features or attributes, such as shape, size, or color.

3 Algorithm design

The third best practice for LiDAR-based obstacle avoidance is to design efficient and effective algorithms that can perform obstacle detection, tracking, and prediction. Obstacle detection is the task of identifying and locating the obstacles in the LiDAR data, such as their position, orientation, and dimensions. Obstacle tracking is the task of updating and maintaining the state of the obstacles over time, such as their velocity, acceleration, and direction. Obstacle prediction is the task of estimating the future state and behavior of the obstacles, such as their trajectory, intention, and maneuver. These algorithms can help the AV to plan a safe and optimal path and avoid collisions.

6 Ethical and legal issues

The sixth and final best practice for LiDAR-based obstacle avoidance is to consider and address the ethical and legal issues that may arise from the use of the technology. Ethical issues include the privacy, security, and accountability of the LiDAR data and the system, such as who owns, accesses, and controls the data, how the data is stored and used, and who is responsible for any accidents or damages caused by the system. Legal issues include the regulations, standards, and policies that govern the use of the LiDAR technology and the system, such as the safety, liability, and insurance requirements, and the rights and obligations of the users, manufacturers, and authorities. These issues can affect the trust, acceptance, and adoption of the LiDAR technology and the system by the public and the stakeholders.