Performance Improvement of LiDAR Systems for Autonomous Driving Using Fast Cross-Correlation Algorithms

Participating in the research project titled "Performance Improvement of LiDAR Systems for Autonomous Driving Using Fast Cross-Correlation Algorithms" has been one of the most intellectually fulfilling experiences of my academic journey. This work examined how advanced signal processing techniques can enhance the performance of LiDAR sensing systems used in autonomous vehicles and environmental mapping.

The primary focus of the research was to design a Fast Cross-Correlation (fCC) algorithm that improves both the speed and accuracy of LiDAR distance measurements, particularly in environments with strong background noise such as sunlight.

Through this project, I gained hands-on experience in sensor technology, signal processing, and experimental system evaluation while collaborating closely with my academic mentor, Professor Mark Radosevich.

Understanding LiDAR Challenges

Autonomous vehicles depend on several sensing technologies to perceive their environment, including cameras, radar, ultrasonic sensors, and LiDAR. Among these technologies, LiDAR stands out because it can generate highly accurate three-dimensional point clouds that represent the surrounding environment.

However, LiDAR systems encounter significant challenges in real-world environments. Strong background light from the sun or artificial sources can introduce noise into the received signal, making accurate distance estimation more difficult.

Our research aimed to address this challenge by developing a faster and more robust signal-processing method capable of improving LiDAR performance even under high-noise conditions.

Designing the LiDAR Measurement System

To conduct our experiments, we designed a single-beam LiDAR system consisting of three major components:

• Laser signal generation unit

• Signal receiving unit

• Signal processing and digitization unit

The system utilized a laser diode driven by a 1 MHz signal generator, while the receiver employed a Single-Photon Avalanche Diode (SPAD) sensor capable of detecting extremely weak reflected signals.

Both transmitted and received signals were digitized using high-speed analog-to-digital converters operating at 125 Mega samples per second.

This experimental setup allowed us to perform controlled measurements at distances ranging from 10 meters to 140 meters, under both daytime and nighttime conditions.

Developing the Fast Cross-Correlation Algorithm

The core innovation of this research was the development of the Fast Cross-Correlation (fCC) algorithm, designed to significantly reduce the computational complexity involved in LiDAR signal processing.

Traditional cross-correlation methods analyze entire waveform signals, which can require millions of arithmetic operations per measurement, slowing down real-time LiDAR processing.

To overcome this limitation, the algorithm introduced two key improvements:

1. Stop Signal Accumulation

Instead of analyzing individual received signals, multiple measurement cycles are accumulated to improve the signal-to-noise ratio (SNR).

2. Reduced Cross-Correlation Processing

The algorithm correlates a narrow transmitted pulse with the accumulated signal, dramatically reducing the number of required computations.

This method enables the LiDAR system to perform distance calculations much faster while maintaining reliable accuracy.

Enhancing Measurement Accuracy with Interpolation

Although the fCC algorithm improved computational speed, measurement accuracy can still be limited by the sampling resolution of the analog-to-digital converter.

To overcome this limitation, several interpolation techniques were applied around the correlation peak to estimate the time-of-flight (ToF) with sub-sample precision.

The four interpolation methods evaluated were:

• Parabolic interpolation

• Gaussian interpolation

• Cosine interpolation

• Cubic spline interpolation

Among these approaches, Cubic Spline Interpolation (fCCS) demonstrated the best performance in terms of accuracy and precision.

Experimental Results and Performance Evaluation

Extensive outdoor experiments were conducted to evaluate the effectiveness of the proposed algorithm under strong background noise conditions.

The results showed significant improvements in both processing speed and measurement accuracy.

Key findings included:

• The fCC algorithm dramatically reduced computational complexity compared to traditional cross-correlation methods.

• The fCCP method achieved processing speeds approximately 38 times faster than conventional approaches.

• The fCCS interpolation technique provided the highest measurement accuracy, achieving centimeter-level precision.

These results demonstrate that the proposed method can significantly enhance LiDAR performance for real-time applications such as autonomous driving and robotic perception.

Broader Impact of the Research

This research contributes to the development of advanced perception systems for intelligent transportation and autonomous vehicles.

Improving LiDAR processing speed and measurement accuracy is critical for enabling real-time decision-making in autonomous systems.

Faster signal processing allows vehicles to respond more rapidly to environmental changes, while improved accuracy ensures reliable detection of obstacles and road features.

Beyond autonomous driving, these techniques can also be applied to fields such as:

• Environmental mapping

• Robotics

• Infrastructure monitoring

• Remote sensing

Personal Growth and Research Experience

Participating in this research project significantly strengthened my skills in signal processing, embedded systems, and sensor technology.

Throughout the project, I gained valuable experience in:

• Analyzing real sensor data

• Implementing signal-processing algorithms

• Conducting experimental performance evaluations

• Translating theoretical models into practical engineering solutions

Working closely with Professor Mark Radosevich deepened my understanding of optical sensing systems and LiDAR technology, inspiring me to continue exploring advanced sensing technologies and intelligent systems in future research.

This experience reinforced my passion for developing technologies that bridge the gap between academic research and real-world engineering applications.