Search Results (1 - 2 of 2 Results)

Sort By  
Sort Dir
 
Results per page  

Howard, Shaun MichaelDeep Learning for Sensor Fusion
Master of Sciences (Engineering), Case Western Reserve University, 2017, EECS - Computer and Information Sciences
The use of multiple sensors in modern day vehicular applications is necessary to provide a complete outlook of surroundings for advanced driver assistance systems (ADAS) and automated driving. The fusion of these sensors provides increased certainty in the recognition, localization and prediction of surroundings. A deep learning-based sensor fusion system is proposed to fuse two independent, multi-modal sensor sources. This system is shown to successfully learn the complex capabilities of an existing state-of-the-art sensor fusion system and generalize well to new sensor fusion datasets. It has high precision and recall with minimal confusion after training on several million examples of labeled multi-modal sensor data. It is robust, has a sustainable training time, and has real-time response capabilities on a deep learning PC with a single NVIDIA GeForce GTX 980Ti graphical processing unit (GPU).

Committee:

Wyatt Newman, Dr (Committee Chair); M. Cenk Cavusoglu, Dr (Committee Member); Michael Lewicki, Dr (Committee Member)

Subjects:

Artificial Intelligence; Computer Science

Keywords:

deep learning; sensor fusion; deep neural networks; advanced driver assistance systems; automated driving; multi-stream neural networks; feedforward; multilayer perceptron; recurrent; gated recurrent unit; long-short term memory; camera; radar;

Every, Joshua LeeDevelopment of a Driver Behavior Based Active Collision Avoidance System
Doctor of Philosophy, The Ohio State University, 2015, Mechanical Engineering
Modern passenger and commercial vehicles share many of the same safety systems. Advanced Cruise Control, Anti-lock Brakes and Electronic Stability Control have all been shown to be an effective means of improving safety on both classes of vehicles. Dynamic Brake Support (DBS) is a system which has been implemented successfully on passenger cars but no record of implementation on heavy vehicles has been found. This is largely due to the belief that commercial vehicle drivers, as professionals, apply the brakes more effectively than passenger car drivers, and therefore do not need this system. This document presents a multi-point study of the applicability of DBS to commercial vehicles. Beginning with analyzing commercial vehicle driver braking behavior to show that commercial vehicle driver braking behavior is fundamentally similar to passenger car driver behavior. Therefore, systems that assist passenger car drivers should also assist commercial vehicle drivers. Next, a revised method of braking behavior analysis is proposed to better characterize this behavior and model it stochastically. Based on data indicating that this system could be effective, commercial vehicle driver braking behavior was evaluated to show that braking behavior in emergency situations could be reliably distinguished from behavior in non-emergency situations. This is important in that it allows the system to act only in situations in which it is necessary. Lastly a prototype DBS system is developed and is shown to be effective at reducing vehicle stopping distance and collision velocity in situations in which the vehicle cannot stop.

Committee:

Dennis A. Guenther (Advisor); Gary J. Heydinger (Committee Member); Ahmet Kahraman (Committee Member); Junmin Wang (Committee Member)

Subjects:

Engineering; Mechanical Engineering

Keywords:

Driver Braking; Advanced Driver Assistance Systems; Vehicle Safety; Brake Assist; Collision Avoidance; Simulation; TruckSim; Cosimulation; HIL; Dynamic Brake Support