Switching Trackers for Effective Sensor Fusion in Advanced Driver Assistance Systems

Abstract

Modern cars utilise Advanced Driver Assistance Systems (ADAS) in several ways. In ADAS, the use of multiple sensors to gauge the environment surrounding the ego-vehicle offers numerous advantages, as fusing information from more than one sensor helps to provide highly reliable and error-free data. The fused data is typically then fed to a tracker algorithm, which helps to reduce noise and compensate for situations when received sensor data is temporarily absent or spurious, or to counter the offhand false positives and negatives. The performances of these constituent algorithms vary vastly under different scenarios. In this paper, we focus on the variation in the performance of tracker algorithms in sensor fusion due to the alteration in external conditions in different scenarios, and on the methods for countering that variation. We introduce a sensor fusion architecture, where the tracking algorithm is spontaneously switched to achieve the utmost performance under all scenarios. By employing a Real-time Traffic Density Estimation (RTDE) technique, we may understand whether the ego-vehicle is currently in dense or sparse traffic conditions. A highly dense traffic (or congested traffic) condition would mean that external circumstances are non-linear; similarly, sparse traffic conditions would mean that the probability of linear external conditions would be higher. We also employ a Traffic Sign Recognition (TSR) algorithm, which is able to monitor for construction zones, junctions, schools, and pedestrian crossings, thereby identifying areas which have a high probability of spontaneous, on-road occurrences. Based on the results received from the RTDE and TSR algorithms, we construct a logic which switches the tracker of the fusion architecture between an Extended Kalman Filter (for linear external scenarios) and an Unscented Kalman Filter (for non-linear scenarios). This ensures that the fusion model always uses the tracker that is best suited for its current needs, thereby yielding consistent accuracy across multiple external scenarios, compared to the fusion models that employ a fixed single tracker.