Understand the application design scheme of autonomous driving radar sensors in one article

Sensors are key components of driverless cars. The ability to monitor the distance to vehicles ahead, behind or to the side provides vital data to the central controller. Optical and infrared cameras, lasers, ultrasound and radar can all be used to provide data about the surrounding environment, roadways and other vehicles. For example, cameras can be used to detect markings on the road to keep vehicles in the correct lane. This is already used to provide lane departure warning in driver assistance systems (ADAS). Today's ADAS systems also use radar for collision detection warnings and adaptive cruise control, where the vehicle can follow the vehicle in front.

Without driver input, self-driving cars require more sensor systems, often using multiple inputs from different sensors to provide a higher level of assurance. These sensor systems are adapting from proven ADAS implementations, although system architecture is changing to manage a wider range of sensors and higher data rates. ​

​Radar Usage

With the increasing adoption of ADAS systems for adaptive cruise control and collision detection, 24 GHz The cost of radar sensors is falling. These are now becoming requirements for car manufacturers to achieve Europe's five-star NCAP safety rating.

For example, Infineon Technologies’ BGT24M 24GHz radar sensor can be used with an external microcontroller in an electronic control unit (ECU) to modify the throttle to maintain contact with the vehicle ahead. constant distance, ranging up to 20 m, as shown in Figure 1.

Figure 1: Infineon Technologies’ automotive radar sensing system.

Many automotive radar systems use the pulse Doppler method, in which the transmitter operates for a short period of time, called the Pulse Repetition Interval (PRI), and then the system switches to receive mode until The next one fires a pulse. When the radar returns, the reflections are coherently processed to extract the range and relative motion of the detected objects.

Another method is to use continuous wave frequency modulation (CWFM). This uses a continuous carrier frequency that changes over time and the receiver is constantly turned on. To prevent the transmit signal from leaking into the receiver, separate transmit and receive antennas must be used.

The BGT24MTR12 is a silicon germanium (SiGe) sensor for signal generation and reception operating from 24.0 to 24.25 GHz. It uses a 24 GHz fundamental voltage controlled oscillator and includes a switchable frequency prescaler with output frequencies of 1.5 GHz and 23 kHz.

An RC polyphase filter (PPF) is used for LO quadrature phase generation of the downconverting mixer, while output power sensors and temperature sensors are integrated into the device for monitoring .

Figure 2: Infineon Technologies’ BGT24MTR12 radar sensor.

The device is controlled via SPI, fabricated on 0.18 µm SiGe:C technology, has a cutoff frequency of 200 GHz, and is available in a 32-pin leadless VQFN package.

However, the architecture of autonomous vehicles is changing. Rather than being local to the ECU, data from various radar systems around the vehicle is fed to a central high-performance controller that combines the signals with signals from cameras and possibly lidar laser sensors.

The controller can be a high-performance general-purpose processor with a graphics control unit (GCU), or a field-programmable gate array where signal processing can be handled by dedicated hardware. This places greater emphasis on analog front-end (AFE) interface devices that must handle higher data rates and more data sources.

The types of radar sensors being used are also changing. 77 GHz sensor provides longer range and higher resolution. The 77 GHz or 79 GHz radar sensor can be adjusted in real time to provide long-range sensing up to 200 m within a 10° arc, for example to detect other vehicles, but it can also be used for wider 30° sensing up to 30 m Lower range arc. Higher frequencies provide greater resolution, allowing radar sensor systems to distinguish between multiple objects in real time, such as detecting many pedestrians within a 30° arc, giving controllers of autonomous vehicles more time and more data.

The 77 GHz sensor uses a silicon germanium bipolar transistor with an oscillation frequency of 300 GHz. This allows one radar sensor to be used in a variety of safety systems such as forward alert, collision warning and automatic braking, and the 77 GHz technology is also more resistant to vehicle vibrations, so less filtering is required.

Figure 3: Different use cases for radar sensors in autonomous vehicles provided by NXP.

# The sensor is used to detect the distance, speed and azimuth of the target vehicle in the vehicle coordinate system (VCS). The accuracy of the data depends on the alignment of the radar sensor.

The radar sensor alignment algorithm is performed while the vehicle is operating at frequencies above 40 Hz. It must calculate the misalignment angle within 1 millisecond based on data provided by the radar sensor as well as vehicle speed, the sensor's position on the vehicle and its pointing angle.

Software tools can be used to analyze recorded sensor data captured from road testing of real vehicles. This test data can be used to develop a radar sensor alignment algorithm that uses a squares algorithm to calculate sensor misalignment angles based on raw radar detections and host vehicle speed. This also estimates the accuracy of the calculated angle based on the residual of the square solution.

02 System Architecture

Analog front ends such as Texas Instruments’ AFE5401-Q1 (Figure 4) can be used to connect radar sensors to The rest of the automotive system is shown in Figure 1. The AFE5401 contains four channels, each containing a low-noise amplifier (LNA), selectable equalizer (EQ), programmable gain amplifier (PGA) and anti-aliasing filter, followed by a high-speed 12-bit analog-to-digital signal of 25 MSPS converter (ADC) per channel. The four ADC outputs are multiplexed on a 12-bit, parallel, CMOS-compatible output bus.

Figure 4: Four channels in Texas Instruments’ AFE5401 radar analog front end can be used with multiple sensors.

For low-cost systems, Analog Devices’ AD8284 provides an analog front end with a four-channel differential multiplexer (mux) that can be used with a programmable gain amplifier Single-channel low-noise preamplifier (LNA) powered by (PGA) and anti-aliasing filter (AAF). This also uses a single direct-to-ADC channel, all integrated with a single 12-bit analog-to-digital converter (ADC). The AD8284 also contains a saturation detection circuit to detect high frequency overvoltage conditions that would otherwise be filtered by the AAF. The analog channel gain range is 17 dB to 35 dB in 6 dB increments, and the ADC conversion rate is up to 60 MSPS. The combined input reference voltage noise for the entire channel is 3.5 nV/√Hz at gain.

The output of the AFE is fed to a processor or FPGA such as Microsemi's IGLOO2 or Fusion or Intel's Cyclone IV. This enables the 2D FFT to be implemented in hardware using FPGA design tools to process the FFT and provide the required data about the surrounding objects. This can then be fed into a central controller.

A key challenge for FPGAs is detecting multiple objects, which is more complex for CWFM architectures than pulse Doppler. One approach is to vary the duration and frequency of the ramp and evaluate how the detected frequencies move through the spectrum with different frequency ramp steepness. Since the ramp can change in 1 ms intervals, hundreds of changes can be analyzed per second.

Figure 5: CWFM radar front end used with Intel’s FPGA.

Fusion of data from other sensors can also help, as camera data can be used to distinguish stronger echoes from vehicles from weaker echoes from people, as well as expected Doppler shift type.

Another option is multi-mode radar, which uses CWFM to find targets at longer distances on the highway, and short-range pulse Doppler radar for easier detection of pedestrians of urban areas.

03 Conclusion

Developments in ADAS sensor systems for autonomous vehicles are changing the way radar systems are implemented. Moving from simpler collision avoidance or adaptive cruise control to all-round detection is a significant challenge. Radar is a very popular sensing technology that is widely accepted among car manufacturers and is therefore the technology for this approach. Combining higher frequency 77 GHz sensors with multimode CWFM and pulse Doppler architectures and data from other sensors such as cameras also poses significant challenges to the processing subsystems. Addressing these challenges in a safe, consistent and cost-effective manner is critical to the continued development of autonomous vehicles.

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