Application design scheme of radar sensor in unmanned vehicle
“Sensors are key components of driverless cars. The ability to monitor the distance to the vehicle ahead, behind or to the side provides important data to the central controller. Optical and infrared cameras, lasers, ultrasound and radar can all be used to provide data about the surrounding environment, roads and other vehicles. For example, cameras can be used to detect markings on the road to keep the vehicle in the correct lane. This has been used to provide lane departure warning in advanced 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.
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Author: European Editors
Sensors are key components of driverless cars. The ability to monitor the distance to the vehicle ahead, behind or to the side provides important data to the central controller. Optical and infrared cameras, lasers, ultrasound and radar can all be used to provide data about the surrounding environment, roads and other vehicles. For example, cameras can be used to detect markings on the road to keep the vehicle in the correct lane. This has been used to provide lane departure warning in advanced 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, autonomous vehicles require more sensor systems, often using multiple inputs from different sensors to provide a higher level of assurance. These sensor systems are being adapted from proven ADAS implementations, although system architectures are changing to manage a wider range of sensors and higher data rates.
With the increasing adoption of ADAS systems for adaptive cruise control and collision detection, the cost of 24 GHz radar sensors is falling. These are now becoming requirements for automakers to achieve Europe’s highest five-star NCAP safety rating.
For example, the BGT24M 24GHz radar sensor from Infineon Technologies can be used with an external microcontroller in the Electronic control unit (ECU) to modify the throttle to maintain a constant distance to the vehicle ahead, with a range of up to 20 m, as shown in the figure 1 shown.
Figure 1: Automotive radar sensing system from Infineon Technologies.
Many automotive radar systems use a pulsed Doppler method in which the transmitter operates for a short period of time, called a pulse repetition interval (PRI), and then the system switches to receive mode until the next transmitted pulse. When the radar returns, the reflections are coherently processed to extract the distance and relative motion of the detected object.
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 VCO and includes a switchable frequency prescaler with output frequencies of 1.5 GHz and 23 kHz.
An RC polyphase filter (PPF) is used for the LO quadrature phase generation of the downconversion mixer, while an output power sensor and temperature sensor are integrated into the device for monitoring.
Figure 2: BGT24MTR12 radar sensor from Infineon Technologies.
The device is SPI controlled, fabricated in 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 driverless vehicles is changing. Instead of a local ECU, data from the various radar systems around the vehicle is fed to a central high-performance controller that combines the signals with signals from cameras and possibly from 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 that can handle signal processing by dedicated hardware. This puts more emphasis on analog front-end (AFE) interface devices that must handle higher data rates and more data sources.
The types of radar sensors in use are also changing. The 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 for detecting other vehicles, but it can also be used for wider 30° sensing 30 m Lower range arc. Higher frequencies provide higher resolution, enabling radar sensor systems to distinguish multiple objects in real time, such as detecting many pedestrians within a 30° arc, providing more time and more data for the controllers of autonomous vehicles.
The 77 GHz sensor uses a silicon germanium bipolar Transistor with an oscillation frequency of 300 GHz. This allows a single 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 better at resisting vehicle vibrations, so less filtering is required.
Figure 3: Different use cases for radar sensors in driverless vehicles provided by NXP.
Sensors are used to detect the distance, velocity and azimuth of the target vehicle in the Vehicle Coordinate System (VCS). The accuracy of the data depends on the precise alignment of the radar sensor.
The radar sensor alignment algorithm executes at frequencies over 40 Hz while the vehicle is running. It has to calculate the misalignment angle within 1 millisecond based on the data provided by the radar sensor as well as the vehicle speed, the position of the sensor on the vehicle and its pointing angle.
Software tools can be used to analyze recorded sensor data captured from road tests of real vehicles. This test data can be used to develop a radar sensor alignment algorithm that uses a least squares algorithm to calculate the sensor misalignment angle based on raw radar detections and host vehicle speed. This also estimates the accuracy of the calculated angle from the residuals of the least squares solution.
system structure
Analog front ends such as Texas Instruments’ (TI) AFE5401-Q1 (Figure 4) can be used to connect the radar sensor to the rest of the automotive system, as shown in Figure 1. The AFE5401 contains four channels, each containing a low noise amplifier (LNA), selectable equalizer (EQ), programmable gain amplifier (PGA), and antialiasing filter, followed by a high-speed 12-bit analog-to-digital at 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 for multiple sensors.
For low-cost systems, Analog Devices’ AD8284 provides an analog front end with a four-channel differential multiplexer (mux) for a single-channel low-noise front end with a programmable gain amplifier (PGA) and anti-aliasing filter. preamplifier (LNA) power supply (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 includes a saturation detection circuit to detect high frequency overvoltage conditions that would otherwise be filtered by the AAF. The analog channels have a gain range of 17 dB to 35 dB in 6 dB increments, and the ADC can convert up to 60 MSPS. At maximum gain, the combined input-referenced voltage noise of the entire channel is 3.5 nV/√Hz.
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 a 2D FFT to be implemented in hardware using FPGA design tools to process the FFT and provide the required data about 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 ramps and assess how the detected frequencies move across the spectrum with different frequency ramp steepnesses. Since the ramp can be changed in 1 ms intervals, hundreds of changes can be analyzed per second.
Figure 5: The CWFM radar front end is used with Intel’s FPGA.
Data fusion 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 the type of Doppler shift to expect.
Another option is multimode radar, which uses CWFM to find targets at longer distances on highways, and short-range pulse-Doppler radar for urban areas where pedestrians are more easily detected.
in conclusion
The development of ADAS sensor systems for driverless vehicles is changing the way radar systems are implemented. Moving from simpler collision avoidance or adaptive cruise control to all-around detection is a major challenge. Radar is a very popular sensing technology that has gained wide acceptance among automakers and is therefore the leading technology for this approach. Combining higher frequency 77 GHz sensors with multimode CWFM and pulsed Doppler architectures and data from other sensors such as cameras also presents significant challenges to the processing subsystem. Addressing these challenges in a safe, consistent, and cost-effective manner is critical for the continued development of autonomous vehicles.
Author: European Editors
Sensors are key components of driverless cars. The ability to monitor the distance to the vehicle ahead, behind or to the side provides important data to the central controller. Optical and infrared cameras, lasers, ultrasound and radar can all be used to provide data about the surrounding environment, roads and other vehicles. For example, cameras can be used to detect markings on the road to keep the vehicle in the correct lane. This has been used to provide lane departure warning in advanced 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, autonomous vehicles require more sensor systems, often using multiple inputs from different sensors to provide a higher level of assurance. These sensor systems are being adapted from proven ADAS implementations, although system architectures are changing to manage a wider range of sensors and higher data rates.
With the increasing adoption of ADAS systems for adaptive cruise control and collision detection, the cost of 24 GHz radar sensors is falling. These are now becoming requirements for automakers to achieve Europe’s highest five-star NCAP safety rating.
For example, the BGT24M 24GHz radar sensor from Infineon Technologies can be used with an external microcontroller in the electronic control unit (ECU) to modify the throttle to maintain a constant distance to the vehicle ahead, with a range of up to 20 m, as shown in the figure 1 shown.
Figure 1: Automotive radar sensing system from Infineon Technologies.
Many automotive radar systems use a pulsed Doppler method in which the transmitter operates for a short period of time, called a pulse repetition interval (PRI), and then the system switches to receive mode until the next transmitted pulse. When the radar returns, the reflections are coherently processed to extract the distance and relative motion of the detected object.
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 VCO and includes a switchable frequency prescaler with output frequencies of 1.5 GHz and 23 kHz.
An RC polyphase filter (PPF) is used for the LO quadrature phase generation of the downconversion mixer, while an output power sensor and temperature sensor are integrated into the device for monitoring.
Figure 2: BGT24MTR12 radar sensor from Infineon Technologies.
The device is SPI controlled, fabricated in 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 driverless vehicles is changing. Instead of a local ECU, data from the various radar systems around the vehicle is fed to a central high-performance controller that combines the signals with signals from cameras and possibly from 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 that can handle signal processing by dedicated hardware. This puts more emphasis on analog front-end (AFE) interface devices that must handle higher data rates and more data sources.
The types of radar sensors in use are also changing. The 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 for detecting other vehicles, but it can also be used for wider 30° sensing 30 m Lower range arc. Higher frequencies provide higher resolution, enabling radar sensor systems to distinguish multiple objects in real time, such as detecting many pedestrians within a 30° arc, providing more time and more data for the controllers of autonomous vehicles.
The 77 GHz sensor uses a silicon germanium bipolar transistor with an oscillation frequency of 300 GHz. This allows a single 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 better at resisting vehicle vibrations, so less filtering is required.
Figure 3: Different use cases for radar sensors in driverless vehicles provided by NXP.
Sensors are used to detect the distance, velocity and azimuth of the target vehicle in the Vehicle Coordinate System (VCS). The accuracy of the data depends on the precise alignment of the radar sensor.
The radar sensor alignment algorithm executes at frequencies over 40 Hz while the vehicle is running. It has to calculate the misalignment angle within 1 millisecond based on the data provided by the radar sensor as well as the vehicle speed, the position of the sensor on the vehicle and its pointing angle.
Software tools can be used to analyze recorded sensor data captured from road tests of real vehicles. This test data can be used to develop a radar sensor alignment algorithm that uses a least squares algorithm to calculate the sensor misalignment angle based on raw radar detections and host vehicle speed. This also estimates the accuracy of the calculated angle from the residuals of the least squares solution.
system structure
Analog front ends such as Texas Instruments’ (TI) AFE5401-Q1 (Figure 4) can be used to connect the radar sensor to the rest of the automotive system, as shown in Figure 1. The AFE5401 contains four channels, each containing a low noise amplifier (LNA), selectable equalizer (EQ), programmable gain amplifier (PGA), and antialiasing filter, followed by a high-speed 12-bit analog-to-digital at 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 for multiple sensors.
For low-cost systems, Analog Devices’ AD8284 provides an analog front end with a four-channel differential multiplexer (mux) for a single-channel low-noise front end with a programmable gain amplifier (PGA) and anti-aliasing filter. preamplifier (LNA) power supply (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 includes a saturation detection circuit to detect high frequency overvoltage conditions that would otherwise be filtered by the AAF. The analog channels have a gain range of 17 dB to 35 dB in 6 dB increments, and the ADC can convert up to 60 MSPS. At maximum gain, the combined input-referenced voltage noise of the entire channel is 3.5 nV/√Hz.
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 a 2D FFT to be implemented in hardware using FPGA design tools to process the FFT and provide the required data about 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 ramps and assess how the detected frequencies move across the spectrum with different frequency ramp steepnesses. Since the ramp can be changed in 1 ms intervals, hundreds of changes can be analyzed per second.
Figure 5: The CWFM radar front end is used with Intel’s FPGA.
Data fusion 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 the type of Doppler shift to expect.
Another option is multimode radar, which uses CWFM to find targets at longer distances on highways, and short-range pulse-Doppler radar for urban areas where pedestrians are more easily detected.
in conclusion
The development of ADAS sensor systems for driverless vehicles is changing the way radar systems are implemented. Moving from simpler collision avoidance or adaptive cruise control to all-around detection is a major challenge. Radar is a very popular sensing technology that has gained wide acceptance among automakers and is therefore the leading technology for this approach. Combining higher frequency 77 GHz sensors with multimode CWFM and pulsed Doppler architectures and data from other sensors such as cameras also presents significant challenges to the processing subsystem. Addressing these challenges in a safe, consistent, and cost-effective manner is critical for the continued development of autonomous vehicles.
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