This lecture addresses one of the hottest technological debates in the modern automobile industry: selecting radar or LiDAR technologies for autonomous and active safety-equipped vehicles. The lecture presents a structured comparison of the two technologies, focusing on their distinct operational characteristics, advantages, and limitations. Radar and LiDAR operate at different frequency bands, which leads to significant differences in how they interact with the environment. Radar is favored for its robustness in adverse weather and low-light conditions, providing long-range detection at a relatively low cost. It also allows for flexible integration behind nontransparent vehicle fascia, making it highly adaptable for commercial vehicle use. On the other hand, LiDAR offers superior angular resolution and spatiotemporal consistency, which are critical for tasks such as object recognition and mapping in autonomous driving.
This lecture begins by outlining the core functionalities of radar and lidar systems and exploring how their unique physical properties, such as wavelength and propagation behaviors, influence their performance in various driving scenarios. Key factors such as range, accuracy, detection reliability, and operational costs will be evaluated, along with a detailed analysis of signal processing techniques used by each sensor type. Additionally, the limitations of LiDAR, including its higher cost and sensitivity to environmental factors, will be contrasted with radar's challenges, such as interference and resolution constraints.
By highlighting recent advancements in both technologies, including digital radars with high-resolution imaging capabilities and efforts to reduce the cost of long-range LiDAR, this lecture aims to provide a comprehensive understanding of the trade-offs involved in choosing between radar and LiDAR for different automotive applications. Future research directions, such as the integration of multi-sensor systems and potential cross-modality innovations, will also be discussed to inspire further developments in autonomous vehicle sensing technologies.