Automotive LiDAR and RADAR Integration with CSI-2 Protocol: Unlocking High-Speed Sensor Data for Autonomous Systems

Image courtesy: MIPI Alliance/Various

The MIPI Camera Serial Interface 2 (CSI-2) protocol, developed by the MIPI Alliance, is primarily designed for high-speed serial communication between cameras and host processors in embedded systems, such as in mobile devices, automotive applications, and IoT devices. While CSI-2 is optimized for camera modules transmitting image or video data, it can also be used with sensors like LiDAR and RADAR in specific configurations, particularly in autonomous vehicles or robotics, where high-bandwidth data transfer is required. Below, I explain how LiDAR and RADAR systems interface with the CSI-2 protocol, their operational principles, and considerations for using CSI-2 with these sensors.

Overview of LiDAR and RADAR Operation

1. LiDAR (Light Detection and Ranging):

LiDAR uses laser pulses to measure distances by calculating the time-of-flight (ToF) of light reflected from objects. It emits laser beams (typically in the near-infrared range, e.g., 905 nm or 1550 nm) and detects the reflected pulses to create precise 3D point clouds of the environment.

LiDAR generates high-resolution spatial data, often in the form of point clouds (X, Y, Z coordinates and intensity values), which can be voluminous, especially for 3D LiDAR systems that produce millions of points per second.

Applications: Autonomous vehicles, robotics, mapping, and surveying.

2. RADAR (Radio Detection and Ranging):

RADAR uses radio waves (typically in the microwave range, 0.3–100 cm wavelength) to detect objects by measuring the time it takes for emitted waves to reflect back. It calculates range, velocity (via Doppler effect), and sometimes angle of objects.

RADAR typically produces 2D or 4D (with elevation) data with lower resolution than LiDAR, including range, speed, and coarse shape information. Modern 4D RADARs provide improved resolution but still output fewer data points per frame (e.g., ~1,000 points) compared to LiDAR (~100,000+ points).

Applications: Automotive ADAS, weather monitoring, air traffic control.

 

CSI-2 Protocol Overview

Purpose: 

CSI-2 is a high-speed, scalable, and low-power serial interface designed for transmitting image and video data from cameras to processors. It uses a differential high-speed (HS) mode for data transfer and a low-speed single-ended (LP) mode for control signaling.

Key Features:

• Supports 1–4 high-speed differential data lanes (D-PHY) for high-bandwidth data transfer, typically up to 2.5 Gbps per lane.
• Data is transmitted in packets with headers defining data type (e.g., RAW, YUV, or user-defined formats).
• While optimized for pixel-based image data, CSI-2 supports user-defined data types, enabling its use with non-camera sensors like LiDAR and RADAR.
• Suitable for real-time applications like autonomous driving demanding lower latencies.
• Uses I2C or SPI for configuration and control of the sensor.

How LiDAR Works with CSI-2 Protocol

LiDAR systems, particularly 3D LiDARs, generate large amounts of point cloud data, which requires high-bandwidth interfaces for real-time transmission. 

Here’s how LiDAR integrates with CSI-2:

1. Data Generation and Processing:

A LiDAR sensor emits laser pulses and records the time-of-flight and intensity of reflected pulses to generate point cloud data (e.g., X, Y, Z coordinates, intensity).

This raw data is often pre-processed by an onboard controller (e.g., FPGA or ASIC) to format it into a structure compatible with CSI-2, such as a stream of coordinates or compressed point cloud data. 

For example, modern LiDARs like the Intel RealSense L515 use ToF principles and can output depth data in formats suitable for CSI-2 transmission.

2. CSI-2 Integration:

LiDAR data is typically not pixel-based like camera data, so it is formatted into user-defined data packets (MIPI CSI-2 supports custom data types via Data Type Codes). For instance, point cloud data can be serialized as a stream of coordinate and intensity values.

CSI-2’s multi-lane D-PHY supports the high data rates needed for 3D LiDAR, which may generate hundreds of thousands to millions of points per second. For example, a 128-channel LiDAR outputting 100,000 points per frame at 10 FPS requires significant bandwidth, which CSI-2 can handle with multiple lanes.

The LiDAR sensor’s pulsing and data acquisition are synchronized with the CSI-2 interface to ensure real-time transmission. This may involve embedding timestamps or frame markers in the data packets.

I2C or SPI is used to configure the LiDAR sensor (e.g., scan rate, resolution) via the CSI-2 control channel.

3. Example Use Case:

In autonomous vehicles, a LiDAR sensor (e.g., Velodyne or Luminar) generates a 3D point cloud that is transmitted over CSI-2 to an NVIDIA Jetson or Qualcomm Snapdragon processor for real-time processing. The CSI-2 interface ensures low-latency delivery for tasks like obstacle detection and mapping.[](https://www.dpvtransportation.com/sensor-suite-autonomous-vehicle-sensors-cameras-lidar-radar/)

4. Challenges:

LiDAR’s high data rate can strain CSI-2’s bandwidth, requiring efficient compression or multiple data lanes.

Non-Standard Data: Since CSI-2 is camera-centric, custom firmware or drivers may be needed to handle LiDAR’s point cloud data.

Power Consumption: High-speed CSI-2 transmission increases power usage, which may be a concern for battery-powered systems.

How RADAR Works with CSI-2 Protocol

RADAR systems generate less data than LiDAR due to lower resolution, but they still require robust interfaces for real-time applications. Here’s how RADAR integrates with CSI-2:

1. Data Generation and Processing:

RADAR emits radio waves and measures the time-of-flight and Doppler shift of reflected signals to determine range, velocity, and sometimes angle.

The output is typically a set of detections (range, speed, azimuth) or, in 4D RADARs, a coarse point cloud with elevation data. This data is processed by an onboard controller into a format suitable for CSI-

2.

For example, a 4D RADAR may output ~1,000 points per frame, significantly less than LiDAR, making it more manageable for CSI-2.

2. CSI-2 Integration:

Data Formatting: RADAR data is packaged into CSI-2 user-defined packets, similar to LiDAR. For instance, range and velocity data can be serialized as structured arrays.

Lower Bandwidth Needs: Due to RADAR’s lower resolution, CSI-2’s single or dual-lane configurations are often sufficient, reducing complexity compared to LiDAR.

Synchronization: RADAR data is synchronized with the CSI-2 interface, often with frame-based or event-based triggers to align with other sensors (e.g., cameras, LiDAR).

I2C or SPI configures RADAR parameters like frequency band or scan rate via the CSI-2 control interface.

3. Example Use Case:

In automotive ADAS, a RADAR sensor (e.g., Texas Instruments AWR1843) transmits range and velocity data over CSI-2 to a central ECU for sensor fusion with camera and LiDAR data. This enables features like adaptive cruise control or collision avoidance.

4. Challenges:

RADAR’s coarse resolution requires integration with other sensors (e.g., cameras) for object classification, which may complicate CSI-2 data handling. While RADAR itself is robust to interference, the CSI-2 interface must be shielded from electromagnetic noise in automotive environments. Some RADAR systems use automotive-specific interfaces like CAN or Ethernet, so adapting to CSI-2 may require additional hardware.

Key Considerations for Using CSI-2 with LiDAR and RADAR

1. Bandwidth Requirements:

LiDAR generates significantly more data than RADAR due to its high-resolution 3D point clouds. 

CSI-2’s multi-lane D-PHY (up to 2.5 Gbps per lane) is well-suited for LiDAR, but high-end systems may require compression or advanced configurations (e.g., MIPI A-PHY/ASA for automotive use).

RADAR’s lower data rate makes it easier to handle with CSI-2, often requiring fewer lanes.

2. Data Type Compatibility:

CSI-2 is designed for pixel-based data, so LiDAR and RADAR data must be formatted into custom packets. This requires firmware development to map point clouds or detection lists to CSI-2’s data structure.

3. Sensor Fusion:

In autonomous vehicles, LiDAR and RADAR data are often fused with camera data. 

CSI-2 is ideal for this, as it is commonly used for camera modules, allowing a unified interface for all sensors. For example, a system might use CSI-2 to transmit camera images, LiDAR point clouds, and RADAR detections to a single processor for real-time fusion.

4. Real-Time Performance:

Both LiDAR and RADAR require low-latency data transfer for applications like autonomous driving. CSI-2’s high-speed mode ensures minimal delay, but the host processor must be capable of handling the incoming data stream.

5. Hardware Requirements:

LiDAR and RADAR sensors need a CSI-2-compatible controller (e.g., FPGA or SoC) to format and transmit data. This may add cost and complexity compared to traditional interfaces like UART or Ethernet.

6. Environmental Robustness:

RADAR’s radio waves are less affected by weather (e.g., fog, rain) than LiDAR’s laser pulses, but the CSI-2 interface itself is unaffected by environmental conditions, as it is a wired connection.

Comparison of LiDAR and RADAR with CSI-2

Practical Examples

Autonomous Vehicles: 

Companies like Waymo and Cruise use LiDAR and RADAR with CSI-2 (or similar high-speed interfaces like MIPI A-PHY) to transmit data to central ECUs for sensor fusion. 

For instance, a Velodyne LiDAR might send point clouds over CSI-2 to an NVIDIA DRIVE platform, while a Bosch RADAR sends velocity data for complementary processing.

Robotics: 

A robot using a 2D LiDAR (e.g., RPLIDAR A1) might interface with a Raspberry Pi via CSI-2 for obstacle detection, with data formatted as a 2D point cloud.

Consumer Devices: 

Apple’s iPhone LiDAR (introduced in iPhone 12 Pro) uses a CSI-2-like interface to transmit depth data for AR applications, demonstrating compact integration.

Conclusion

LiDAR and RADAR can interface with the CSI-2 protocol by formatting their data (point clouds for LiDAR, detection lists for RADAR) into user-defined packets, leveraging CSI-2’s high-speed, low-latency capabilities. LiDAR requires more bandwidth due to its high-resolution 3D data, while RADAR’s lower data rate is easier to handle. 

Both benefit from CSI-2’s compatibility with camera systems, enabling sensor fusion in applications like autonomous driving. 

However, challenges include custom data formatting, high computational demands for LiDAR, and ensuring robust integration in noisy environments. 

For optimal performance, system designers must carefully match the sensor’s data rate to CSI-2’s bandwidth and ensure compatibility with the host processor.