Camera, Radar and LiDAR: A Comparison of the Three Types of Sensors and Their Limitations

Autonomous driving is enabled by two sets of technologies: V2X and ADAS. V2X (vehicle-to-everything) utilizes wireless communication technology to facilitate real-time interactions between the vehicle and its surrounding objects and infrastructure. On the other hand, ADAS (advanced driver-assistance systems) make use of built-in sensors to detect and calculate the surrounding environment. Both technologies complement each other to ensure a safe and seamless autonomous driving experience. We have so far explained how V2X technology works and the different wireless communication standards involved, see: DSRC vs. C-V2X: A Detailed Comparison of the 2 Types of V2X Technologies. In this article, we will focus on the technologies behind ADAS and take a deep dive into the three types of commonly used sensors: camera, radar, and LiDAR.


First introduced in the form of a backup camera by Toyota in 1991, camera is the oldest type of sensor used in vehicles. It is also the most intuitive sensor since it works just like our eyes do. After decades of usage for backup assistance, car cameras had undergone significant improvements in the 2010s as they were applied for lane keep and lane centering assists. Today, camera has become the most essential component of the ADAS and can be found in every vehicle.

Advantages of Camera:

Vision-like sensory. Just like our vision, cameras can easily distinguish shapes, colours, and quickly identify the type of object based on such information. Hence, cameras can produce an autonomous driving experience that is very similar to the one produced by a human driver.

Recognizing 2D information. Since camera is based on imagery, it is the only sensor with the capability of detecting 2D shapes and colours, making it crucial to reading lanes and pavement markings. With higher resolutions, even fading lines and shapes can be read very accurately. Infrared lighting is also equipped with most modern cameras, making it just as easy to navigate at night.

Low cost. Camera is relatively cheaper compared to other types of sensors. This made it possible for OEMs to introduce better autonomous driving features to mid-range and even lower-end vehicles.

Disadvantages of Camera:

Poor vision under extreme weather events. Its similarity to the human eye also makes it a major disadvantage under severe weather conditions like snowstorms, sandstorms, or other conditions leading to low visibility. Therefore, the camera is only as good as the human eye. Nevertheless, most people do not expect their car to see better than their eyes and would not fully rely on their car under such extreme conditions. In fact, Tesla had decided to abandon radar and use camera only for its Autopilot system, starting with its newly produced Model 3 and Model Y vehicles. Named Tesla Vision, the system is expected to decrease the frequency of system glitches because of the reduction of confusing signals from radar.


Radar (radio detection and ranging) was first invented prior to World War II and has been widely used since then to precisely track the position, speed, and direction of aircraft and ships. It was first brought into cars by Mercedes-Benz in 1999 to support its adaptive speed feature. Radar technology can be broken down into a transmitter and a receiver. The transmitter blasts radio waves in a targeted direction. These radio waves then get reflected when they reach any significant object. The receiver picks up these reflected waves and analyzes them to identify the location, speed, and direction of the object.

Advantages of Radar:

Unaffected by weather conditions. The greatest advantage of radar is that the transmission of radio waves is not affected by visibility, lighting, and noise. Therefore, radar performance is consistent across all environmental conditions.

Default sensor for emergency braking. The radar system has been used as the default sensor for emergency braking due to its ability to detect and forecast moving objects coming into the vehicle’s path.

Disadvantages of Radar:

Low-definition modeling. The radio waves are highly accurate at detecting objects. Yet, compared to the camera, radar is relatively weak at modeling a perfectly precise shape of the object. As a result, the system might not be able to identify exactly what the object is. For instance, unlike the camera, the radar system normally cannot distinguish bicycles from motorcycles, even though it has no problem determining their speeds.


LiDAR (light detection and ranging) adopted its name the same way as radar did. Despite its underlying mechanism being similar to radar, LiDAR utilizes laser lights instead of radio waves. Invisible laser lights are fired to the vehicle’s surroundings. The computer then uses the reflection time paired with the speed of light to calculate the distance of the reflector.

Advantages of LiDAR:

High-definition 3D modeling. LiDAR can be seen as a more advanced version of radar. It has a detection range of as far as 100 meters away with a calculation error of less than two centimeters. Hence it is capable of measuring thousands of points at any moment, allowing it to model up a very precise 3D depiction of the surrounding environment.

Unaffected by weather conditions. Same as radar, LiDAR’s efficacy is not affected by the environmental condition.

Disadvantages of LiDAR:

Highly sophisticated. In order to provide an accurate 3D model of the environment, LiDAR calculates hundreds of thousands of points every second and transforms them into actions. This means that LiDAR requires a significant amount of computing power compared to camera and radar. It also makes LiDAR prone to system malfunctions and software glitches.

High cost. As expected, due to the sophistication of the software and the computing resources needed, the price to implement a set of LiDAR sensors is the highest among the three.

Are Sensors Reliable?

All three types of sensors have their pros and cons. Therefore, most OEMs use a mix of at least two of the three to complement each other and outweigh their weaknesses. As sensor technologies become more mature, more and more vehicles are expected to reach autonomous driving levels 3 to 4 in the next five years.

Yet, no matter how advanced and sophisticated sensor technologies become, they are nothing more than computers; and connected computers are always at risk of cyberattacks. Therefore, just as we trust these sensors to take over our wheels, cybersecurity measures must be in place to ensure they do not get tampered with by malicious actors. The automotive cybersecurity regulation outlined by WP.29 ensures that all OEMs build their vehicles with secure cybersecurity systems in place, so that we can all trust the sensors to do their job.

AutoCrypt IVS is an in-vehicle security solution chosen by some of the top ten OEMs in the world for vehicular cybersecurity type approval. Not only does IVS block malicious threats from outside the vehicle, but it also monitors communications within the vehicle, and responds to abnormal and malicious activity in real-time.

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