REITS: Reflective Surface for Intelligent Transportation Systems

Autonomous vehicles are predicted to dominate the transportation industry in the foreseeable future. Safety is one of the major challenges to the early deployment of self-driving systems. To ensure safety, self-driving vehicles must reliably sense and detect humans, other vehicles, and road infrastructure accurately, robustly, and timely. In this paper, we describe the design of REITS and simulate its operation. REITS is an antenna system for blind beamforming that uses constructive interference to return an enhanced radar signal in the direction of the radar emitter. The signal-to-noise ratio (SNR) of the return signal is further enhanced by canceling out irrelevant reflections, using destructive interference. Combining blind beamforming with the enhanced SNR improves the detection distance of a self-driving car radar by a factor of2.82.

[1]  Eric P. Xing,et al.  CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving , 2018, ECCV.

[2]  Nidhi Kalra,et al.  Driving to Safety , 2016 .

[3]  Melanie Grunwald.  Fundamentals Of Electromagnetic Phenomena , 2016 .

[4]  E. Sharp,et al.  Van Atta reflector array , 1960 .

[5]  Joshua R. Smith,et al.  LoRa Backscatter , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[6]  Swarun Kumar,et al.  Pushing the Range Limits of Commercial Passive RFIDs , 2019, NSDI.

[7]  R.J. Mailloux,et al.  Phased array theory and technology , 1981, Proceedings of the IEEE.

[8]  Wolfram Burgard,et al.  Learning driving styles for autonomous vehicles from demonstration , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Joshua R. Smith,et al.  LoRa Backscatter: Enabling The Vision of Ubiquitous Connectivity , 2017 .

[10]  Narciso García,et al.  Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  James J. Little,et al.  The Raincouver Scene Parsing Benchmark for Self-Driving in Adverse Weather and at Night , 2017, IEEE Robotics and Automation Letters.

[12]  H. Rohling,et al.  Waveform design principles for automotive radar systems , 2001, 2001 CIE International Conference on Radar Proceedings (Cat No.01TH8559).

[13]  Xiaojiang Chen,et al.  PLoRa: a passive long-range data network from ambient LoRa transmissions , 2018, SIGCOMM.

[14]  Frank Ellinger,et al.  A Millimeter-Wave Low-Power Active Backscatter Tag for FMCW Radar Systems , 2013, IEEE Transactions on Microwave Theory and Techniques.

[15]  Thomas L. Marzetta,et al.  Argos: practical many-antenna base stations , 2012, Mobicom '12.

[16]  Juergen Hasch,et al.  A 77-GHz active millimeter-wave reflector for FMCW radar , 2017, 2017 IEEE Radio Frequency Integrated Circuits Symposium (RFIC).

[17]  Mohammad Hossein Mazaheri,et al.  Millimeter Wave Backscatter: Toward Batteryless Wireless Networking at Gigabit Speeds , 2020, HotNets.

[18]  Ta-Sung Lee,et al.  Design of an FMCW radar baseband signal processing system for automotive application , 2016, SpringerPlus.