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Modern vehicles provide partly automated features such as keeping the car within its lane, speed controls or emergency braking.
Towards level 4 and 5 of SAE’s self-driving automation, where no driver attention is ever required for safety, autonomous vehicles require a variety of sensors to perceive their surroundings, such as radar, lidar and camera.
Despite the advantages of radar technology, currently in many cases automotive manufacturers still are using camera as the primary sensor to make the final safety decisions.
The radar sensor is being used as the secondary sensor; meaning, the vehicle system receives the Radar warning, but decides to take an action only upon the camera sensor verification. The main reason is limitation on angular resolution in the current automotive radar sensors.
The 4D imagining radars are a solution that provide real-time 3D location (range, azimuth and elevation) as well as velocity.
The new generation ultra high-resolution 4D imaging radars are using large number of transmit and receive antennas to be able to provide additional high resolution information that can enable object detection, classification, and tracking. One such novel 4D imaging radar requires to combine functionalities of current medium/short/long range radar sensors by transmitting signals in multi modes. Indeed, imaging radar generally require wideband signals to provide fine range resolution and large azimuth and elevation antenna apertures to obtain high 2D angular resolution. The large aperture can be virtually obtained using the MIMO virtual array concept, which requires transmission of orthogonal waveforms. On the other hand, maximum detection range can be obtained when all the antennas transmit scaled version of one waveform simultaneously and coherently to create a focused beam. This requires a trade-off that can be addressed by 4D imaging radars. The number of transmit antennas can be selected based on a feedback from the environment cognitive scenario where waveform design has a crucial role.