Machine Learning in Advanced Driver-Assistance Systems

In the context of advanced driver-assistance systems (ADAS), vehicles are equipped with multiple sensors to record the vehicle's environment and use intelligent algorithms to understand the data. This study contributes to the research in modern ADAS on different aspects.

Methods deployed in ADAS must be accurate and computationally efficient in order to run fast on embedded platforms. We introduce a novel approach for pedestrian detection that economizes on the computational cost of cascades. We demonstrate that (a) our two-stage cascade achieves a high accuracy while running in real time, and (b) our three-stage cascade ranks as the fourth best-performing method on one of the most challenging pedestrian datasets.

The other challenge faced with ADAS is the scarcity of positive training data. We introduce a novel approach that enables AdaBoost detectors to benefit from a high number of negative samples. We demonstrate that our approach ranks as the second-best among its competitors on two challenging pedestrian datasets while being multiple times faster.

Acquiring labeled training data is costly and time-consuming, particularly for traffic sign recognition. We investigate the use of synthetic data with the aspiration to reduce the human efforts behind the data preparation. We (a) algorithmically and architecturally adapt the adversarial modeling framework to the image data provided in ADAS, and (b) conduct various evaluations and discuss promising future research directions.