In recent years, the competition between LiDAR and other sensor technologies—such as cameras, radar, and ultrasound—has intensified, driving the need for more advanced sensor fusion techniques. This has also led to a greater emphasis on selecting high-quality photodetectors, light sources, and MEMS micromirrors to ensure accurate and reliable performance in autonomous systems.
With ongoing advancements in sensor technology, imaging, radar, LiDAR, electronics, and artificial intelligence, dozens of advanced driver assistance system (ADAS) functions have become standard. These include collision avoidance, blind spot monitoring, lane departure warnings, and parking assistance. These systems rely on sensor fusion to synchronize their operations, enabling fully automated or unmanned vehicles to detect their surroundings, alert drivers to potential hazards, and even take evasive actions independently to prevent accidents.
Self-driving cars must be capable of identifying and distinguishing objects at high speeds. They use distance measurement techniques to quickly create 3D maps of the road up to 100 meters ahead and generate high-resolution images at distances of up to 250 meters. When no human is present, the car’s AI must make optimal decisions based on real-time data.
One fundamental method used in this process is measuring the time-of-flight (ToF) of energy pulses emitted by the vehicle and reflected back from the target. Once the speed of the pulse through the air is known, the distance to the object can be calculated. These pulses can be ultrasonic (sonar), radio (radar), or light (LiDAR).
Among these ToF technologies, LiDAR stands out due to its higher angular resolution. Unlike radar, which suffers from significant beam divergence, LiDAR offers better object recognition and smaller diffraction, making it ideal for high-speed applications where quick reaction times are crucial. As shown in Figure 1, LiDAR can distinguish between two closely positioned vehicles, while radar often fails to do so.
In ToF LiDAR systems, a laser emits a light pulse with a duration τ, triggering an internal timing circuit. When the reflected pulse reaches the photodetector, it disables the clock, allowing the system to calculate the round-trip time Δt and determine the distance R to the object.
The accuracy of this measurement depends on several factors, including the laser's pulse duration and the photodetector’s time jitter. For a 5 cm distance resolution, the time jitter δΔt and pulse width τ must both be around 300 ps. This requires high-performance photodetectors and short-pulse lasers, such as picosecond lasers, which are more expensive but offer superior precision.
When designing automotive LiDAR systems, choosing the right wavelength is critical. The most common options are 905 nm and 1550 nm. While 905 nm benefits from cost-effective silicon-based photodetectors, 1550 nm is safer for the human eye and allows for higher pulse energy, which is important for maximizing the photon budget.
Atmospheric conditions, such as scattering and absorption, also affect LiDAR performance. Water absorption is stronger at 1550 nm, meaning that 905 nm may experience less signal loss in typical weather conditions.
Photodetector selection plays a key role in LiDAR performance. Only a small portion of the emitted light reaches the detector, and factors like atmospheric attenuation, beam divergence, and target reflectivity all influence the received power. Narrow-band filters help reduce background noise, but they cannot eliminate it entirely, affecting the system’s dynamic range and increasing shot noise.
To create a full 360° x 20° 3D map around a vehicle, LiDAR systems use either scanning methods or flash-based area array approaches. Scanning LiDARs, such as those using rotating mirrors or MEMS micromirrors, offer flexibility but come with mechanical limitations. Flash LiDAR, on the other hand, captures entire scenes at once but faces challenges with photon budget at longer distances.
An alternative to traditional ToF LiDAR is Frequency Modulated Continuous Wave (FMCW) LiDAR. This technology modulates the frequency of the transmitted signal over time, allowing for more precise distance and velocity measurements. It is less susceptible to noise and can provide better performance in complex environments. However, FMCW LiDAR requires more computational power and is generally slower when generating full 3D maps.
Despite these challenges, LiDAR continues to evolve, bringing us closer to a future where fully autonomous vehicles are the norm. With ongoing research and innovation, the dream of self-driving cars is becoming increasingly realistic.
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