o. Although this solution does not degrade the position uncertainty, it ultimately results in poor intersection throughput. We present Crossroads, a time-sensitive programming method to program the interface of a vehicle and the IM. Without requiring additional buffer to account for the effect of network and computational delay, Crossroads enables efficient intersection management. Test results on a 1/10 scale model of intersection using TRAXXAS RC cars demonstrates that our Crossroads approach obviates the need for large buffers to accommodate for the network and computation delay, and can reduce the average wait time for the vehicles at a single-lane intersection by 24%. To compare Crossroads with previous approaches, we perform extensive Matlab simulations, and find that Crossroads achieves on average 1.62X higher throughput than a simple VT-IM with extra safety buffer, and 1.36X better than AIM.
Mission aviation groups operate aircraft in areas with limited infrastructure. Existing airdrop methods pose significant risk due to their lack of steerability. This thesis details the development of Manna, a system built to address these concerns. Manna provides an automated, low cost, safe steerable delivery platform, through a custom designed parafoil and guidance unit. Flight tests and simulations show that Manna can provide a safer alternative for critical air deliveries.
Mission aviation groups operate aircraft in areas with limited infrastructure. Existing airdrop methods pose significant risk due to their lack of steerability. This thesis details the development of Manna, a system built to address these concerns. Manna provides an automated, low cost, safe steerable delivery platform, through a custom designed parafoil and guidance unit. Flight tests and simulations show that Manna can provide a safer alternative for critical air deliveries.
A robust autopilot control system for a ground vehicle was designed, fabricated, and implemented on a remote control car. The autopilot system consists of navigation, guidance, and three controller subsystems. The autopilot’s hardware subsystems are an Arduino processor, GPS receiver, 9 DOF inertial measurement system, and an SD card data logger. A complete system simulation was developed and used to verify the integrated design and algorithms, prior to field testing. The simulation results indicated the system performs as designed, with no anomalous behaviors observed. Simulations were also used to assess and verify each of the three controllers’ robustness qualities. The complete hardware system was field tested and verified fully functional against complex mission scenarios. The system performed as designed, with no anomalous behaviors observed. The system performed successfully in the presence of external disturbances (e.g., rocks, holes, dirt piles in the vehicle’s path), which demonstrated and verified the design is robust. Additional robustness testing consisted of doubling the vehicle’s polar moment of inertia and verifying this did not have any adverse effects on system performance. All the planned tasks were completed and the project’s objectives were met.
The future of driving is largely headed towards autonomous vehicles, and this is clear with companies such as Tesla, Waymo, and even tech giant Apple. Many professionals predict that autonomous vehicles will likely be commercially available and legal to use in some places by the late 2020s [15]. There are some benefits to the rapid development of autonomous vehicle controllers, such as more independence for those who can’t drive due to impairments, the potential for reduced traffic, as well as possibly decreasing the number of accidents. Though these are promising prospects, there are ethical concerns regarding the implementation of such technology. The goal of this thesis is to provide an introductory literature review that discusses the history of autonomous vehicles, different levels of autonomy, ethical considerations in autonomous systems, and prior work on characterizing human driving behaviors and implementing these behaviors with autonomous vehicle controllers. Finally, recommendations are proposed for data collection on human driving behaviors in an ongoing NSF-funded project at Arizona State University, “Embodiment of Human Values Profiles in Autonomous Vehicles via Psychomimetic Controller Design.”