LiDAR vs. Cameras: Which Technology is Better for Self-Driving Cars?

If you’re a Tesla or self-driving car fanatic, you must have heard about the LiDAR vs. camera debate. Tesla’s big bet is on cameras for its Autopilot full self-driving systems because the company’s CEO, Elon Musk, believes using LiDAR will make cars expensive, ugly, and unnecessary. Plus, people drive using their eyes (vision), not by shooting lasers out of their eyes. But companies like Waymo believe LiDAR is better due to its advanced detection capabilities. Let’s look at the pros and cons of each one to see who’s right.

What Is LiDAR?

LiDAR (Light Detection and Ranging) is a distance measurement technology that uses laser light pulses to measure distances to objects. To do this, LiDAR operates using these 5 steps.

• Laser Emission: The system emits short pulses of laser light directionally (towards the front of the vehicle).
• Reflection Detection: A sensor in the LiDAR system detects the laser light pulse reflections from the target, which is the area or landscape ahead of the vehicle.
• Time Measurement: The system precisely measures the time taken for the time to be reflected back from the target or objects in front.
• Distance Calculation: Distance is a product of speed and time. Since the laser pulses travel at the speed of light, LiDAR can calculate the distance to each object ahead using the time measurement input.
• 3D Mapping: As the LiDAR system scans the area ahead of the vehicle (doing the four steps above repeatedly), it creates a 3D representation of the captured data to form a map.

What Are Cameras for Self-Driving Cars?

Cameras are like digital human eyes. They capture light from the front of the vehicle through a lens, which focuses this light onto a digital sensor for conversion into electrical signals and processing to form an image. Doing this repetitively forms a video that can be analyzed continuously to determine if and when objects appear ahead.

LiDAR vs. Cameras for Self-Driving Cars

Pros of LiDAR

• High Accuracy: The laser pulses and algorithms running in LiDAR systems create precise and detailed data points cloud of the surrounding environment. This generates a 3D map showing precise sizes and shapes of objects and distances to them, which is better for autonomous driving.
• Operates Effectively in Low Light Conditions: Cameras rely on ambient light to capture images, meaning low light conditions can affect object recognition. But LiDAR does not rely on this light. Instead, it shoots laser pulses to detect reflections, so these systems can function effectively in low-light conditions.
• Provides Privacy: The LiDAR points cloud doesn’t contain personally identifiable information data (license plates, faces, etc.). Laser pulse reflections can’t capture this data, which gives LiDAR a privacy advantage.
• Long Range: LiDAR has a longer detection range than cameras, which is better for high-speed autonomous driving.

Cons of LiDAR

• High Cost: LiDAR is costlier than cameras due to its manufacturing complexity, advanced processing, and high-performance requirements.
• Affected by Certain Weather Conditions: Heavy fog, snow, and rain affect the accuracy of LiDAR systems because water can absorb or scatter the laser pulses.
• Vulnerable to Strong Light Sources: Lightbars, headlights, laser pulses from other LiDAR systems, and intense sunlight can saturate the reflection detector in LiDAR systems, making it difficult to differentiate between the required reflected laser light and noise.

Pros of Using Cameras

• Superior Object Recognition: High-res cameras capture detailed images of the road ahead, which enables easy scene understanding and superb object recognition.
• Compact and Lightweight: Cameras are tinier and lighter than LiDAR systems, which explains why Elon Musk stated that LiDAR makes cars ugly. Cameras can be easily integrated with various devices in the vehicle, but LiDAR systems are usually placed above the car, introducing a hump that is more noticeable.
• Low Cost: Cameras are generally cheaper than LiDAR systems.

Cons of Using Cameras

• Ambient Light Dependency: Cameras rely on ambient light to detect the light coming off objects in the scene ahead. As such, they are not as reliable when it’s dark.
• Affected by Certain Weather Conditions: Heavy snow, rain, or fog also degrade the camera’s ability to capture images of the road ahead.
• Requires AI or Powerful Image Processing: To enable full autonomous driving, vision-based systems require extensive AI training to understand the scene ahead for precise object detection.
• Privacy Concerns: Cameras can capture personally identifiable information, and this is the basis on which the US government is looking to ban Chinese self-driving cars from US soil. The US thinks China will use these cars to collect sensitive data on Americans using their onboard cameras, which can lead to national security issues.

Conclusion: Which Technology is Better?

Tesla might refine its camera-based vision detection system with time, but as we stand now, the combination of both cameras and LiDAR is the best for self-driving vehicles. Either way, these systems need reliable power and signal transmission cable assemblies, which you can get from Cloom Tech at reasonable prices. Cloom Tech specializes in manufacturing and assembling cable assemblies for such projects, with full customization to match the application. This means using specialized connectors and terminals, abrasion-resistant and high temperature materials for the harness, and waterproof jackets for the camera and LiDAR-system wires. Each autonomous driving project is unique, and Cloom Tech will help you develop it fully and reliably. Contact them to inquire about their capabilities and partnership to actualize your project.