SUMMARY: This talk will present an overview of recent research at UW FUNLab around the use of vehicular radar for advanced driver assistance systems (en route to a future vision of autonomous driving). Wideband (typically FMCW or chirp) radars are increasingly deployed onboard vehicles as key high-resolution sensors for environmental mapping or imaging and various safety features. The talk will be demarcated into two parts, centered on the evolving role of radar ‘cognition’ in complex operating environments to address two important future challenges:
 

1. Mitigating multi-access interference among Radars (e.g., dense traffic scenario)
   This will first illustrate the impact of mutual interference on detection performance in commercial Chirp/FMCW radars and then highlight some multi-access protocol design approaches for effective resource sharing among multiple radars.
2. Contributions to radar vision via new radar hardware (MIMO radar) + associated advanced signal processing (Synthetic Aperture) principles using Convolutional Neural Network (‘Radar Net’) based machine learning approaches for enhanced object detection/classification in challenging circumstances.