Exploring Millimeter-Wave Radar Algorithms for Obstacle Detection and Tracking

06/27/2024

Robust real-time obstacle detection and tracking remain significant challenges in the automotive industry. Historically, sensors like LiDAR and cameras have dominated this space, but recent technological advancements have enabled the resurgence of radar technology, specifically millimeter-wave (mmWave) radar sensors. MmWave radar sensors prove to be more robust to adverse weather conditions than other commonly used sensors. This work focuses on developing an obstacle detection and tracking algorithm that relies exclusively on data from these radars. The developed algorithm starts by accumulating recent frames of radar data and then employs DBSCAN for obstacle detection and Kalman Filters for tracking. Results indicate that this approach effectively tracks various types of obstacles. However, it demonstrates superior performance in tracking obstacles moving away from the sensor compared to those moving toward it. This discrepancy arises from the intrinsic properties of radar technology. Nonetheless, with proper radar configuration and processing, this difference can be mitigated. This approach serves as a method for evaluating the capabilities of mmWave radar systems in obstacle detection and tracking while robust Neural Networks trained specifically for this problem are not yet feasible, mainly due to the extensive data requirements for proper training.

Authors in the community:

• 

  R

  Rafael Pedro Carvalho

  ist193164

Supervisors of this institution:

• 

  A

  Alberto Manuel Martinho Vale

  ist13968

electrical-engineering-electronic-engineering-information-engineering - Electrical engineering, electronic engineering, information engineering

eng - English

Instituto Superior Técnico