FMCW Radar-Lidar Signal Processing Architecture for Autonomous Vehicles
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Solution Overview
Problem
Current FMCW-based radar and lidar systems for autonomous driving vehicles face high complexity and cost due to modular designs, requiring high computing power and power consumption, which is not efficiently addressed by existing technologies.
Innovation Solution
A centralized high-performance computing architecture for processing FMCW-based radar and lidar signals, utilizing edge devices with multiple processing cores and memory to generate 4D point clouds, synchronize data, and compress information, reducing power consumption and costs while increasing sensor performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If modular radar and lidar sensor designs are used, then sensor performance and computing power are improved, but power consumption and system complexity increase
Solution Approach 1:
The patent combines multiple radar and lidar sensors into a single integrated sensor unit with shared signal processing resources. The modular sensor array architecture allows multiple sensors to share common ADCs, FFT processors, and control logic, reducing redundant power consumption while maintaining individual sensor performance capabilities.
2Reliability
If modular radar and lidar sensor designs are used, then sensor performance is improved, but manufacturing cost and system complexity increase
Solution Approach 1:
The sensor unit employs universal signal processing components that can handle both radar and lidar signals through the same processing pipeline. The FFT processor, ADC, and control logic are designed to be multi-functional, accepting input from multiple sensor types and processing them through unified algorithms, thereby reducing overall system complexity.
3Productivity
If high computing power is allocated to each sensor, then signal processing capability is improved, but power consumption and cost increase
Solution Approach 1:
The signal processing function is segmented and distributed across multiple processing cores within the sensor unit. Instead of allocating full processing power to each individual sensor, the system divides the processing workload among several cores that can handle signals from multiple sensors simultaneously, reducing total power consumption while maintaining high processing capability.
Data Source
AI summary
In one embodiment, a frequency-modulated continuous-wave (FMCW) radar-lidar system for an autonomous driving vehicle (ADV) includes one or more lidar frontends configured to generate one or more frames of lidar data. The system includes one or more radar frontends configured to generate one or more frames of radar data. The system includes a plurality of input/output (I/O) interfaces, each corresponding to one of the one or more lidar or radar frontends to receive the radar or lidar data. The system includes an edge device coupled to the plurality of input/output (I/O) interfaces, where the edge device receives the radar data or lidar data for processing to generate a set of 4D point clouds from the radar or lidar data, and the set of 4D point clouds are used to perceive a surrounding environment of the ADV.