Experimental evaluation of a look-ahead controller for a heavy-duty vehicle with varying velocity demands

Slope Shape and Edge Intelligent Recognition Technology Based on Deep Neural Sensing Network

“…It includes the input layer and other basic structural layer models [14]. Held et al used AlexNet to win the photo contest classification [15]. Since then, deep learning has developed rapidly and steadily in the key visual part of the computer.…”

Autonomous Avoidance Maneuvers for Vehicles using Optimization

“…Offline optimization allows generation of fuel-saving look-up tables for velocity and freewheeling under varying speed limit [29] and to improve controller robustness when parameters like tire cornering stiffness and body moment of inertia are known only as ranges [35]. In [36], optimal braking patterns using offline optimization have been formulated and interpreted to provide new insights for future safety systems with adaptation of the level of braking.…”

“…Examples of additional ways to make autonomous vehicles safer include: motion planning with focus on safe stop trajectories [21]; a computationally efficient departure prediction algorithm [22]; and a model predictive controller formulation for trucks with included factors related to controller stability [23]. Especially for large vehicles, questions on how to model and formulate motion problems [24,25] and how to keep trip time while decreasing fuel consumption in a computationally efficient way [26][27][28][29] are important as well.…”

Hierarchical Rule-Base Reduction Fuzzy Control for Path Tracking Variable Linear Speed Differential Steer Vehicles

“…Similar work was seen using reinforcement learning in the context of car traffic [19]. In a non-highway heavy-duty ground vehicle setting, velocity control was implemented in [20] by solving an optimal control problem to reduce fuel usage. Additionally, the HRBR method, used in [17] and further discussed in Section II, was employed to solve the ballooning rule-base issue associated with increasing the number of membership functions and linguistic variables in fuzzy systems.…”