LIDAR

LiDAR — Light Detection and Ranging — is a sensor technology that functions similar to radar, but using light. A LiDAR sensor emits a series of invisible lights (usually within the 600 – 1000 nm spectrum), which reflect off any objects in range and return to the sensor (Sivaraman and Manubhai 2013). A computer analyzes the patterns from the returning light, and uses the data to infer a variety of environmental and behavioral information, like object classification, distance, and velocity relative to the sensor, among others (Lange et al 2016). The resulting product is a densely spaced network of highly accurate georeferenced distance points, often called a point cloud. The primary benefit of using light over radio waves is that the shorter wavelengths of light provide much greater accuracy than radar.

There are three types of LiDAR hardware: aerial LiDAR, stationary surveying LiDAR, and mobile LiDAR (Davis, 2016).

• Aerial LiDAR is the use of airborne LiDAR scanners mounted on aircraft to characterize the surface of the earth. The most common use is the generation of digital terrain models for elevation; however, it can also be used to collect data about man-made objects such as buildings, roads, or traffic flow on roads. The systems used for aerial LiDAR generally combine LiDAR scanning technology, which provides information about the geometry of the ground, with positioning instruments such as Global Navigation Satellite Systems/Global Positioning System (GNSS/GPS), inertial measurement units (IMUs), or other positioning technology to determine the position and path of the airplane. This technique is the least commonly used of the LiDAR approaches for freight traffic data collection.
• Stationary LiDAR systems are mounted on a base and slowly move to scan the area around it creating high accuracy, information-rich point clouds. These types of systems have many applications including traffic data collection, tolling, surveying and construction, forestry and agriculture, among others. Deployment of stationary LiDAR is easy, especially if the sensor is mounted on a vehicle that can be parked near the fixed point of data collection.
• Mobile LiDAR is the use of LiDAR technology on terrestrial vehicles. It is used to capture information about a vehicle’s surroundings including the road geometry, roadside obstacles, other vehicles, and more. This data can be used in many different ways from creating maps to real-time use in autonomous vehicle systems. Mobile LiDAR captures information about a corridor that is more detailed than aerial LiDAR but is faster to collect than setting up multiple scans with a stationary LiDAR product.

Like radar, LiDAR can be used for freight vehicle detection, extraction, and tracking, which form the basis for traffic flow parameter estimation, such as vehicle count, classification and vehicle velocity estimates. This vehicle classification ability makes LiDAR useful for measuring truck volumes at specific locations.

LiDAR technology for transportation is not new. However, sensors and devices in forms that are portable and cost-effective for transportation applications such as vehicle classification and object recognition have been steadily evolving over time. Recent advances in LiDAR technology include measurement of the “brightness” of returning laser beams, an increased number of recorded returns from a single laser beam (4 to 5), and a frequency of scans approaching over a hundred times per second. These advances support the expansion of LiDAR technology from simple surface extraction to more demanding feature extraction applications (Sivaraman and Manubhai 2013).

The LiDAR system (which is made up of the LiDAR sensor, a GPS receiver, and an inertial measurement unit) emits intense focused beams of light and measures the time it takes for the reflections to be detected by the sensor. This information is used to compute ranges, or distances, to objects. Differences in laser return times and wavelengths can then be used to make digital 3D-representations of the target. The 3-D coordinates (i.e., x,y,z or latitude, longitude, and elevation) of the target objects are computed from the time difference between the laser pulse being emitted and returned, the angle at which the pulse was emitted, and the absolute location of the sensor on or above the surface of the Earth (Grejner-Brzezinska et al 2007). The images at right show stock footage of a similar vehicle to the one shown in the LiDAR image (Brandesky, n.d.). The LiDAR figure is clear enough to discern axles and vehicle’s windows. Height and depth measurements (Y and Z axes) give even more information about the detected vehicle.

• Regulatory Environment: Facilitates data access and use.
• Ownership: Not tightly controlled.
• Privacy: Current applications typically protect private information

There are no specific regulatory or privacy issues to be addressed with LiDAR technology. The captured data are not directly associated with any specific user. More than likely the sensors would be owned and deployed by the public agency so that data ownership should not be a concern.

• Capacity: Fairly easy to work with data.
• Stewardship: Captures large amounts of data that complicates data storage and management.
• Equity: Data representative of specific roadway users.

The deployment and use of LiDAR technology is not complicated. Most over-roadway sensors are compact and not roadway invasive, making installation and maintenance relatively easy. The cost of LiDAR sensors has been decreasing rapidly with the growing market for small and cost-effective sensors for automated vehicle purposes. Data stewardship is more onerous than for radar systems because LiDAR they produce a large amount of data, which can be unwieldy and require careful data management practices (Olsen, et al 2013).

Because LiDAR maps the environment in three dimensions, multiple times per second, and at a high resolution, these sensors can generate a large amount of data very quickly. If an organization wishes to store and use LiDAR data, the entity should develop data management practices to help manage and make sense of the large volume of data.

• Completeness: Data gaps exist due to functionality issues.
• Accuracy: Limitations due to functionality issues.
• Verifiability: Access to raw data but data is voluminous presenting verification challenges.
• Dynamism: Time from capture to analysis is lengthened due to enormity of data processing requirements.
• Durability: Low cost and ease of deployment and use ensure its future stability as a source of data.

The primary advantages of LiDAR as an efficient and accurate mapping tool are: the sensor is unaffected by differences between day and night conditions, it produces a continuous data stream, and fast and accurate measurement of the road surface and structure mainly depends on the quality of the direct georeferencing based on GPS/INS integration, not the LiDAR sensor itself.

LiDAR sensors suffer from some limitations, like susceptibility to inclement weather, and as such, are often paired with other sensor types (Sivaraman and Manubhai 2013). Radars and digital cameras are common pairing options to mitigate a LIDAR’s individual weaknesses and bolster data robustness.

LIDAR sensors function using light, and as such, when objects in the air block or scatter the light, the sensor will fail or erroneously detect “ghost objects” (Rasshofer and Gresser 2005). Rain, snow, fog, dust, and other weather can reduce the efficacy of LiDAR sensors (BITRE 2014). As a general rule of thumb, if weather is heavy enough to challenge human drivers’ sight, it will also impair LiDAR sensors.

Data verifiability could be a concern because of the large amounts of data that can be produced quickly. However, public agencies have access to the raw data (though voluminous) which helps mitigate the verifiability of data. Because the cost of the sensors is decreasing and the use is expanding into autonomous vehicle applications, the durability of the technology is long-term.