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Using Model-Based Design, engineers model complex systems and simulate them on their desktop environment for analysis and design purposes. Model-Based Design supports a wide variety of C/C++ code generation applications that include stand-alone simulation, rapid control prototyping, hardware-in-the-loop testing, and production or embedded code deployment. Many of these code generation scenarios impose different requirements on the generated code. Stand-alone simulations usually need to run fast, for parameter sweep or Monte Carlo studies, but do not need to execute in true hard real-time. Hardware-in-the-loop tests by definition use engine control unit (ECU) component hardware that requires a hard real-time execution environment to protect the physical devices. Code generated for production ECUs must satisfy hard real-time, efficiency, legacy code, and other requirements involving verification and validation efforts.

There are many unique aspects related to implementing unmanned agricultural vehicles. Automated navigation systems, drive by wire electronics and implement control are already widely available in the marketplace providing a firm foundation for unmanned vehicles. Variable environments, harsh weather conditions, large vehicles and implements and high costs present challenges to bringing unmanned systems to the agriculture marketplace. The lack of existing standards is another limiting factor that calls for a united effort from industry, academia and government. Despite these factors, unmanned vehicles will be part of the future of agriculture, providing increased productivity and helping to meet the world's growing food needs.

A transformation of agriculture reached commercial reality at the beginning of this century as automated steering of agricultural machine systems increased the productivity and convenience in crop production systems. Following guidance, additional technologies have resulted in increasing optimized machine productivity. Today, integrated worksite solutions through machine and information management continues to transform agriculture. This is the precursor to autonomous worksite solutions that lead to the optimization of the worksite ecosystem. This paper will review the progress from the perspective of the customer value provided by increasing automated systems and the industry execution of autonomous driving technologies and will enable the pathways to autonomous worksites.

Typical approaches to regulating sound performance of vehicles and products rely upon A-weighted sound pressure level or sound power level. It is well known that these parameters do not provide a complete picture of the customer’s perception of the product and may mislead engineering efforts for product improvement. A leading manufacturer of agricultural equipment set out to implement a process to include sound quality targets in its product engineering cycle. First, meaningful vehicle level targets were set for a tractor by conducting extensive jury evaluation testing and by using objective metrics that represent the customer’s subjective preference for sound. Sensitivity studies (“what-if” games) were then conducted, using the predicted sound quality (SQ) index as validation metric, to define the impact on the SQ performance of different noise components (frequency ranges, tones, transients).