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Southwest Research Institute developed an Autonomous Ground Vehicle (AGV) capable of navigating in urban environments. The paper first gives an overview of hardware and software onboard the vehicle. The systems onboard are classified into perception, intelligence, and command and control modules to mimic a human driver. Perception deals with sensing from the world and translating it into situation awareness. This awareness is then fed into intelligence modules. Intelligence modules take inputs from the user to understand the need to navigate from its current location to another destination and, then, generate a path between them on urban, drivable surfaces using its internal urban database. Situational awareness helps intelligence to update the path in real time by avoiding any static/moving obstacles while following traffic rules.

This paper describes the development of a generic test cell software designed to overcome many vehicle-component testing difficulties by introducing modern, real-time control and simulation capabilities directly to laboratory test environments. Successfully demonstrated in a transmission test cell system, this software eliminated the need for internal combustion engines (ICE) and test-track vehicles. It incorporated the control of an advanced AC induction motor that electrically simulated the ICE and a DC dynamometer that electrically replicated vehicle loads. Engine behaviors controlled by the software included not only the average crankshaft torque production but also engine inertia and firing pulses, particularly during shifts. Vehicle loads included rolling resistance, aerodynamic drag, grade, and more importantly, vehicle inertia corresponding to sport utility, light truck, or passenger cars.

Heavy duty trucks account for about 50 percent of the NOx burden in urban areas and consume about 20 percent of the national transportation fuel in the United States. There is a continuing need to reduce emissions and fuel consumption. Much of the focus of current work is on engine development as a stand-alone subsystem. While this has yielded impressive gains so far, further improvement in emissions or engine efficiency is unlikely in a cost effective manner. Consequently, an integrated approach looking at the whole powertrain is required. A computer model of the heavy duty truck system was built and evaluated. The model includes both conventional and hybrid powertrains. It uses a series of interacting sub-models for the vehicle, transmission, engine, exhaust aftertreatment and braking energy recovery/storage devices. A specified driving cycle is used to calculate the power requirements at the wheels and energy flow and inefficiencies throughout the drivetrain.