Small-Scale Intelligent Vehicle Design Platform

Intelligent Vehicle Design is a growing field with the potential to save many lives by actively minimizing the impacts of human error. Though there are many ways to research intelligent vehicle control, full-scale implementations are expensive and dangerous and computer simulations have extremely steep learning curves. Researchers and students need an accessible, adaptable, and robust development platform to rapidly create and test autonomous control algorithms. While small-scale platforms are often designed from the ground up for specific projects, this requires analysis, design, and manufacture. The goal of this project is to develop a small-scale intelligent vehicle that can be configured with physical sensors and programmed with control algorithms designed in Simulink. We will strive to make our design adaptable and reproducible through intentional design and documentation. We have completed the design to adapt a 1/7th scale remote control vehicle with a custom chassis, independently driven wheels, and a Raspberry Pi based control package. An inertial measurement unit, an ultrasonic rangefinder, and a camera will give the system realtime data about itself and its surroundings. This well-documented research platform will enable more students to get hands on experience in developing and testing intelligent vehicle systems. These students will become the next generation of vehicle safety engineers, developing the life-saving intelligent vehicle systems of the future

Autonomous Vehicles Operating Collaboratively to Avoid Debris and Obstructions

The purpose of this project is to demonstrate the safety and increased fuel efficiency of an automated collision avoidance system in collaborative vehicle platooning. This project was cosponsored by Daimler Trucks North America headquartered in Portland, Oregon, as well as Dr. Birdsong, and Dr. DeBruhl of Cal Poly. The mechanical engineering team consists of Cole Oppenheim, James Gildart, Toan Le, and Kyle Bybee who worked in coordination with a team of computer engineers. Vehicle platooning is a driving technique to increase the fuel efficiency of a group of vehicles by following a lead vehicle closely to reduce the drag experienced by the group. Specifically, large tractor trailer trucks could become more efficient utilizing vehicle platooning. To implement this system most effectively would require an automatic system for collision avoidance. The goal for the mechanical engineering team working on this project was build and design two scale model vehicles, a test track, and dynamic models of the vehicles. These were then interface with computer vision software and hardware (created in collaboration of a team of computer engineers) that allows the vehicles to autonomously platoon and avoid objects that would otherwise cause a collision. Interactions with the computer engineering team occurred at minimum on a weekly basis and more whenever necessary. Interactions between the team’s original occurred as meetings to determine each team individual progress until integration could be accomplished. When the systems were being integrated, meetings occurred regularly (2-3 times a week) to ensure the vehicles could properly execute their design function. The goal of this project is to demonstrate how this system could be implemented in truck platooning safely and to demonstrate the advantages of platooning with system developed. This project was intended and will be presented to compete at the Enhanced Safety of Vehicles conference in the Netherlands in June of 2019. This report covers the scope of work of this project, the preliminary design direction, and the final design direction, and the final design for the assembly of the two 1/10 scale cars, the track design, and the controls strategy to interface with the CPE’s software

Advancing Intersection Management by Utilizing Cost Effective Intelligent Vehicle Concepts and Vehicle-to-Infrastructure Communication Techniques

Intelligent Transport Systems (ITS) is a growing field of research which focuses on the alleviation of traffic congestion and road accidents caused by miscommunication or confusion of human drivers. Intelligent Intersection Management is a subdivision of ITS which focuses on the seamless management of vehicles arriving at, traversing and exiting intersections to prevent congestion and collision within or around the intersection. This research sought to develop a cost effective method of implementing wireless Vehicle-to-Infrastructure (V2I) communication based Intelligent Intersection Management, by employing the use of 1:4.5 scale version autonomous vehicle prototypes, on a similarly scaled four-way intersection. This was accomplished by employing Robot Operating System (ROS) on a single board computer platform which communicated comma-separated integers via Zigbee XBee radio transceivers to prioritize navigation of vehicles arriving at the intersection based on arrival time and the vehicles’ projected paths

Daimscale — 1:14th Tractor-Trailer for Testing Driver Assistance Technology

Active driver assistance systems are becoming increasingly wide-spread throughout the automotive industry due to their potential for safer roads and decreased costs of transportation, but testing these systems on real trucks can be time consuming, dangerous, and costly. Testing these systems on a small-scale tractor-trailer combination will lead to faster and more efficient development of driver assistance systems and can be used by both engineers and students, leading to a larger field of experienced developers to improve these systems. Our goal will be to design, manufacture, and build a scale 6x2 model of the tractor portion of a Daimler semi-truck as well as a generic trailer. Both of these components must have adequate similitude to the original tractor-trailer in order to model the vehicle dynamics of a semi-truck so new driver assistance systems can be accurately tested. To do this, the chassis, suspension geometry, center of gravity, inertial properties, steering radius, tires, acceleration/braking curves, and other aspects need to be analyzed. This truck must be able to withstand minor rolls, jackknifes, and low speed collisions as well as be able to be run for long periods of time with minimal mechanical maintenance

Optimal Direct Yaw Moment Control of a 4WD Electric Vehicle

This thesis is concerned with electronic stability of an all-wheel drive electric vehicle with independent motors mounted in each wheel. The additional controllability and speed permitted using independent motors can be exploited to improve the handling and stability of electric vehicles. In this thesis, these improvements arise from employing a direct yaw moment control (DYC) system that seeks to adapt the understeer gradient of the vehicle and achieve neutral steer by employing a supervisory controller and simultaneously tracking an ideal yaw rate and ideal sideslip angle. DYC enhances vehicle stability by generating a corrective yaw moment realized by a torque vectoring controller which generates an optimal torque distribution among the four wheels. The torque allocation at each instant is computed by finding a solution to an optimization problem using gradient descent, a well-known algorithm that seeks the minimum cost employing the gradient of the cost function. A cost function seeking to minimize excessive wheel slip is proposed as the basis of the optimization problem, while the constraints come from the physical limitations of the motors and friction limits between the tires and road. The DYC system requires information about the tire forces in real-time, so this study presents a framework for estimating the tire force in all three coordinate directions. The sideslip angle is also a crucial quantity that must be measured or estimated but is outside the scope of this study. A comparative analysis of three different formulations of sliding mode control used for computation of the corrective yaw moment and an evaluation of how successfully they achieve neutral steer is presented. IPG Automotive’s CarMaker software, a high-fidelity vehicle simulator, was used as the plant model. A custom electric powertrain model was developed to enable any CarMaker vehicle to be reconfigured for independent control of the motors. This custom powertrain, called TVC_OpenXWD uses the torque/speed map of a Protean Pd18 implemented with lookup tables for each of the four motors. The TVC_OpenXWD powertrain model and controller were designed in MATLAB and Simulink and exported as C code to run them as plug-ins in CarMaker. Simulations of some common maneuvers, including the J-turn, sinusoidal steer, skid pad, and mu-split, indicate that employing DYC can achieve neutral steer. Additionally, it simultaneously tracks the ideal yaw rate and sideslip angle, while maximizing the traction on each tire[CB1] . The control system performance is evaluated based on its ability to achieve neutral steer by means of tracking the reference yaw rate, stabilizing the vehicle by means of reducing the sideslip angle, and to reduce chattering. A comparative analysis of sliding mode control employing a conventional switching function (CSMC), modified switching function (MSMC), and PID control (HSMC) demonstrates that the MSMC outperforms the other two methods in addition to the open loop system

Reinforcement Learning Approach to Design Practical Adaptive Control for a Small-Scale Intelligent Vehicle

Reinforcement learning (RL) based techniques have been employed for the tracking and adaptive cruise control of a small-scale vehicle with the aim to transfer the obtained knowledge to a full-scale intelligent vehicle in the near future. Unlike most other control techniques, the purpose of this study is to seek a practical method that enables the vehicle, in the real environment and in real time, to learn the control behavior on its own while adapting to the changing circumstances. In this context, it is necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. Meanwhile, in order to realize adaptive cruise control specifically, a set of symmetrical control actions consisting of steering angle and vehicle speed needs to be optimized simultaneously. In this paper, firstly, the experimental setup of the small-scale intelligent vehicle is introduced. Subsequently, three model-free RL algorithm are conducted to develop and finally form the strategy to keep the vehicle within its lanes at constant and top velocity. Furthermore, a model-based RL strategy is compared that incorporates learning from real experience and planning from simulated experience. Finally, a Q-learning based adaptive cruise control strategy is intermixed to the existing tracking control architecture to allow the vehicle slow-down in the curve and accelerate on straightaways. The experimental results show that the Q-learning and Sarsa (λ) algorithms can achieve a better tracking behavior than the conventional Sarsa, and Q-learning outperform Sarsa (λ) in terms of computational complexity. The Dyna-Q method performs similarly with the Sarsa (λ) algorithms, but with a significant reduction of computational time. Compared with a fine-tuned proportion integration differentiation (PID) controller, the good-balanced Q-learning is seen to perform better and it can also be easily applied to control problems with over one control actions