  There are two main algorithmic approaches to autonomous driving systems: (1)
An end-to-end system in which a single deep neural network learns to map
sensory input directly into appropriate warning and driving responses. (2) A
mediated hybrid recognition system in which a system is created by combining
independent modules that detect each semantic feature. While some researchers
believe that deep learning can solve any problem, others believe that a more
engineered and symbolic approach is needed to cope with complex environments
with less data. Deep learning alone has achieved state-of-the-art results in
many areas, from complex gameplay to predicting protein structures. In
particular, in image classification and recognition, deep learning models have
achieved accuracies as high as humans. But sometimes it can be very difficult
to debug if the deep learning model doesn't work. Deep learning models can be
vulnerable and are very sensitive to changes in data distribution.
Generalization can be problematic. It's usually hard to prove why it works or
doesn't. Deep learning models can also be vulnerable to adversarial attacks.
Here, we combine deep learning-based object recognition and tracking with an
adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System
(NARS), that can adapt to its environment by building concepts based on
perceptual sequences. We achieved an improved intersection-over-union (IOU)
object recognition performance of 0.65 in the adaptive retraining model
compared to IOU 0.31 in the COCO data pre-trained model. We improved the object
detection limits using RADAR sensors in a simulated environment, and
demonstrated the weaving car detection capability by combining deep
learning-based object detection and tracking with a neurosymbolic model.
