Development of vision-based soft sensing techniques with training in virtual environment for autonomous vehicle control

The goal of this master thesis is to develop an original approach to lane estimation for scaled vehicles using a front-mounted camera and convolutional neural networks. The key components of this estimation process are the fact that all the training is performed in simulation using a noisy path; and the online inference is performed on low-end hardware (Raspberry Pi 4) in an efficient and responsive way, while being very accurate. The heading error of the standard pure pursuit controller is chosen as estimation target. A clothoid based centerline has been chosen as training path for its several advantages in the analyzed scenario. Different performance metrics are evaluated and the standard deviation of the error is found to be the more effective. An analysis on the hyperparameters (image dimension, lookahead distance, training variability, and others) is performed in order to find the best combinations and evaluate the impact of each parameter. From the results in a real world scenario a very small network and image and a very high training variability resulted as the best overall combination, with the network complexity and training variability playing a major role in the accuracy of the system. The whole process is finally tested in a real life control loop achieving very good performance, allowing for precise lane tracking using delayless local estimation.The goal of this master thesis is to develop an original approach to lane estimation for scaled vehicles using a front-mounted camera and convolutional neural networks. The key components of this estimation process are the fact that all the training is performed in simulation using a noisy path; and the online inference is performed on low-end hardware (Raspberry Pi 4) in an efficient and responsive way, while being very accurate. The heading error of the standard pure pursuit controller is chosen as estimation target. A clothoid based centerline has been chosen as training path for its several advantages in the analyzed scenario. Different performance metrics are evaluated and the standard deviation of the error is found to be the more effective. An analysis on the hyperparameters (image dimension, lookahead distance, training variability, and others) is performed in order to find the best combinations and evaluate the impact of each parameter. From the results in a real world scenario a very small network and image and a very high training variability resulted as the best overall combination, with the network complexity and training variability playing a major role in the accuracy of the system. The whole process is finally tested in a real life control loop achieving very good performance, allowing for precise lane tracking using delayless local estimation

Data-dependent cycle-accurate power modeling of RTL-level IPs using machine learning

In a chip design project, early design planning has a strong impact on the schedule and the cost of design. Power estimation is part of early design planning, and it greatly affects design decisions. Power modeling performed at a high level of abstraction is fast but inaccurate due to lack of circuit switching activity information. By contrast, power modeling performed at a low level of abstraction is more accurate as the synthesized circuit synthesis is known, but this simulation is typically slow. This report explores a power modeling approach performed at register transfer level (RTL). It exploits machine learning models in order to have a fast yet relatively accurate cycle-by-cycle power estimation. The approach is data-dependent, where cycle-specific models are trained based on the switching activity of signals obtained from RTL simulation and cycle-by-cycle power values obtained from a reference gate-level simulation of an existing RTL design. Therefore, if any changes are applied to the RTL design, re-training of models is required. The approach aims at obtaining fast yet accurate power predictions for new invocations of a given trained model using signal activity information collected during simulation of the unmodified RTL. At a low level, the complete visibility of signals in a design unintuitively might cause overtraining the model leading to inaccurate estimation. The suggested model employs automatic feature selection in each cycle. Based on the invocations used to train the cycle-by-cycle models, only signals that may switch during a given cycle will be selected as the features for their respective cycle-specific model. The method was tested on an 8-by-8 DCT design and the power estimates were within 6.5% of those from a commercial power analysis tool. This report also simulates and compares the approach of cycle-specific models to the approach of a single global model for all cycles and show that the cycle-specific approach is twice as accurate.Electrical and Computer Engineerin