Design of Path Tracking Controller for Autonomous Vehicles Through Bias Learning of Vehicle Dynamic Models Under Environmental Uncertainty

Path tracking error control is an important functionality in the development of autonomous vehicles when a collision-free path has been planned. Large path tracking errors could lead to collision or even out of the control of the vehicle. Vehicle dynamic models are used to minimize the vehicle path tracking error so that control strategies can be designed under different scenarios. However, the vehicle dynamic model may not truly represent the actual vehicle dynamics. Furthermore, the nominal parameter employed in the vehicle dynamic model cannot represent actual operating conditions of the vehicle under environmental uncertainty. This paper presents a learning-based bias modeling method to improve the fidelity of any baseline vehicle dynamics model so that effective path tracking controller design can be achieved through a low fidelity but high-efficiency vehicle dynamic model with the aid of a few experiments or high fidelity simulations. The state-of-the-art of machine learning models, such as Gaussian process (GP) regression, recurrent neural network (RNN), and long short-term memory (LSTM) network, are employed for bias learning and comparison. A high-fidelity vehicle simulator, CARLA, is employed to collect virtual test data and demonstrate the effectiveness of the proposed bias-learning based control strategies under environmental uncertainty.

Attention constraints and learning in categories

When different stimuli belong to the same category, learning about their attributes should be guided by this categorical structure. Here, we demonstrate how an adaptive response to attention constraints can bias learning toward shared qualities and away from individual differences. In three preregistered experiments using an information sampling paradigm with mousetracking, we find that people preferentially attend to information at the category level when idiosyncratic variation is low, when time constraints are more severe, and when the category contains more members. While attention is more diffuse across all information sources than predicted by Bayesian theory, there are signs of convergence toward this optimal benchmark with experience. Our results thus indicate a novel way in which a focus on categories can be driven by rational principles.

Improve 3D laser scanner measurements accuracy using a FFBP neural network with Widrow-Hoff weight/bias learning function

Many laser scanners depend on their mechanical construction to guarantee their measurements accuracy, however, the current computational technologies allow us to improve these measurements by mathematical methods implemented in neural networks. In this article we are going to introduce the current laser scanner technologies, give a description of our 3D laser scanner and adjust their measurement error by a previously trained feed forward back propagation (FFBP) neural network with a Widrow-Hoff weight/bias learning function. A comparative analysis with other learning functions such as the Kohonen algorithm and gradient descendent with momentum algorithm is presented. Finally, computational simulations are conducted to verify the performance and method uncertainty in the proposed system.