Radar depth association with vision detected vehicles on a highway

Abstract

No single sensor can independently predict the 3-D environment around us. A vision sensor helps locate objects in a 2-D plane. However, estimating distance using one vision sensor has a limitation. A single radar sensor returns the range of objects accurately; however, the complexity and cost increase if good spatial resolution is required. Thus a radar does not indicate which range corresponds to which object. In this paper, we associate vision detected objects to radar returned echoes, focusing on highways, estimating the 3-D locations of cars around an ego vehicle. This information would help cars driving autonomously to maneuver around.

Extending Reliability of mmWave Radar Tracking and Detection via Fusion With Camera

“…However, Lidar works on the infrared band, which is (i) prone to interference, (ii) cost-intensive and (iii) bulky in size. Not many studies have been conducted with associating heterogeneous sensors like cameras or infrared sensors with radars [14], [15], primarily due to the fact that the camera's and radar's detections are unrelated and difficult to align without certain assumptions. In this case, we are implementing HEM and creating a track-oriented association and fusion algorithm for calibration and tracking.…”

“…Some papers [14], [15], [20], [21] illustrated the alignment between radars and other heterogeneous sensors. They assumed radars and other sensors are working at identical principle.…”

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

“…Hence, choosing a manufacturer that can offer stable software updates is important. mmWave radars have been integrated with Lidars in [35,36] to provide better results; additionally, radars fused with cameras or infrared sensors are studied in [37][38][39]. Texas Instruments (TI) introduced a commercial radar, TDA3x, board for radar camera fusion to provide effective tracking and detection applied in [40].…”