@conference {458, title = {Adaptive HSL Filters and Inverse Perspective Transforms in Lane Detection for Autonomous Driving}, booktitle = {CAT Vehicle REU}, year = {2019}, month = {03/2019}, keywords = {autonomous vehicles, computer vision, image processing}, doi = {10.13140/RG.2.2.18919.60327}, url = {http://doi.org/10.13140/RG.2.2.18919.60327}, author = {Hannah Mason and Landon Bentley and Joe MacInnes and Rahul Bhadani and Tamal Bose} } @conference {457, title = {A Pseudo-Derivative Method for Sliding Window Path Mapping in Robotics-Based Image Processing}, year = {2019}, publisher = {CAT Vehicle Research Experience for Undergraduates}, organization = {CAT Vehicle Research Experience for Undergraduates}, type = {Undergraduate Research}, address = {Accepted at IEEE International Conference on Robotic Computing}, abstract = {

A sliding window technique in robotics-based image processing applications is a common approach to path mapping from extracted features. Mapping a path inside an image requires finding a series of points representing the path. Previous approaches find these points by sliding a window along the path in fixed increments across one image dimension. After each slide, the center of the window in the other dimension is adjusted so that the window maximally covers the path in that area. This approach, however, fails to map paths that experience sharp curvature since the windows slide along only one dimension. The method proposed herein uses a pseudo-derivative approach to sliding windows that improves upon the traditional technique by dynamically adjusting the windows along both image dimensions during each slide. In this method, the directional components of a vector representing the previous slide are used as a naive estimation to perform the current slide. If this fails to map the path, the vector direction is used to enlarge the window dimensions. The method was tested in the domain of autonomous vehicles as an approach for detecting road lane markings. The algorithm proved more successful than previous sliding window approaches on perspective mapped lane images.

}, keywords = {autonomous vehicles, image processing}, doi = {10.13140/RG.2.2.35628.10885}, author = {Landon Bentley and Joe MacInnes and Rahul Bhadani and Tamal Bose} } @conference {470, title = {Reinforcement Learning for Autonomous Driving using CAT Vehicle Testbed}, booktitle = {CAT Vehicle Research Experience for Undergraduates}, year = {2019}, month = {08/2019}, publisher = {The University of Arizona}, organization = {The University of Arizona}, type = {Technical Report}, address = {Tucson}, abstract = {

We discuss a deep reinforcement learning implementation using the CAT Vehicle Testbed and discuss the merits of simulation-based deep reinforcement learning. After implementing a simple 3 layered neural network which learned using deep Q-learning, we found some challenges associated with ROS and Gazebo. In spite of these challenges, we demonstrate that our reinforcement learning architecture can teach a car to avoid obstacles. With these preliminary results, we discuss what new things we can do and how we can implement more advanced methods.

}, keywords = {Autonomous Systems, Machine Learning}, url = {http://csl.arizona.edu/content/reinforcement-learning-autonomous-driving-using-cat-vehicle-testbed}, author = {John Nguyen and Hoang Huynh and Eric Av and Rahul Bhadani and Tamal Bose} } @conference {468, title = {A Sliding Window for Path Mapping based on a Pseudo-Derivative Method in Autonomous Navigation}, booktitle = {IEEE Vehicular Technology Conference, VTC-2019, Fall}, year = {2019}, month = {11/2019}, publisher = {IEEE}, organization = {IEEE}, address = {Honolulu, Hawaii}, keywords = {autonomous vehicles}, author = {Landon Bentley and Joe MacInnes and Hannah Mason and Rahul Bhadani and Tamal Bose} } @conference {475, title = {Cognitive Engine Testbed for Vehicular Communications}, booktitle = {CAT Vehicle Research Experience for Undergraduates}, year = {2018}, publisher = {The University of Arizona}, organization = {The University of Arizona}, address = {Tucson, Arizona}, abstract = {

Ad-hoc networks have the potential to increase the safety and reliability of autonomous vehicles. The amount of radio spectrum available for such networks is limited, however. The use of cognitive radio, especially when integrated with reinforcement learning algorithms, may help to ease the issue of limited spectrum by finding optimal transmission policies and detecting the presence of other users, especially in a scenario where a Primary User and Secondary User are contesting for spectrum. This paper presents a testbed for simulating cognitive engines in these networks using a variety of reinforcement learning algorithms, including -greedy, Softmax Strategy, and Q-Learning. The goal of these cognitive engines is to learn to choose the best modulation and coding rates given various channel conditions and a user-defined optimization goal (i.e. maximize throughput, minimize bit error rate). The cognitive engine then learns the optimal coding rates and modulation schemes for the given environment, and the testbed displays a visual of the performance of the cognitive engine at each time step. In the case of Q-learning in a Primary User vs. Secondary User scenario, the cognitive engine also learns to choose the best channel for transmission.

}, url = {http://csl.arizona.edu/content/cognitive-engine-testbed-vehicular-communications}, author = {William Anderson and Ashley Aponik and Youssef Daoud and Noel Teku and Garrett Vanhoy and Tamal Bose} } @proceedings {sprinkle271, title = {Power Efficient Vehicular Ad Hoc Networks}, year = {2016}, month = {03/2016}, pages = {26-31}, address = {Reston, VA}, abstract = {Inter-vehicular communication is a growing plat- form for improving roadway safety. The highly mobile nature of Vehicle to Vehicle communications causes rapid changes in network topologies and propagation conditions. Since the advent of Vehicular Ad-Hoc Networks (VANETs), over fifty routing protocols with attendant topologies have been proposed. Despite these protocols{\textquoteright} merits, many of them are not optimized for power management and frequency reuse. Our approach utilizes the one dimensional dynamic of divided highways to simplify the routing problem and reduce energy consumption. Since each car is aware of only two types of connections, up-road and down-road, we can form low power, line of sight links between adjacent vehicles. We also utilize a fuzzy logic algorithm that predicts the location of up-road cars to reduce interference from request for link signals. Once these links have been established, up-road vehicles send data down-road for a length of time based on the relative speed of the two vehicles. After this time period has expired the down-road vehicle must request additional information, restarting the timer. Data sent through the network will include information on up-road vehicles, and when required, messages such as accident notifications, alerts, and traffic warnings. Through simulation, we show that our approach to VANETs maintains its update frequency despite bumper to bumper traffic and uses two to five orders of magnitude less power than an IEEE 802.11 network with clustering and 1 mW transmit power. Overall, the network performs well and is a viable improvement to the standard.}, author = {Sterling Holcomb and Audrey Knowlton and Juan Guerra and Hamed Asadi and Haris Volos and Jonathan Sprinkle and Tamal Bose} } @conference {sprinkle274, title = {Intersection Management via the Opportunistic Organization of Platoons by Route}, booktitle = {WinnComm 2016}, year = {2015}, abstract = {Intersections behave as a pathway for traffic redirection and play an important role in the general movement of traffic, because of this, traffic congestion is not desirable and methods to remove congestion or its effects have been implemented. Generally, congestion occurs when a group of vehicles that is large relative to available road space attempts to move through an area that cannot redirect the group effectively. The are of this paper{\textquoteright}s focus is the intersection. Some methods currently implemented to control congestion are roadway expansion, the use of adaptive traffic lights, and the use of public transportation. Good short term solutions, they only postpone the occurrence of congestion and are also costly. One way to deal with congestion is have vehicles cooperate with each other and their intersections through means of wireless communication. The vehicles can then be organized by a management system in such a way that vehicle throughput at an intersection can be maintained even at high levels of traffic congestion. A requirement of the system proposed in this paper is that all vehicles behave autonomously in order to obtain easily predictable behavior. The following describes the results of such a simulation of such a system. The data gathered over four simulations show that a higher platoon depth causes a general decrease in delay. The results between signalized and un-signalized simulations were vastly different with the un-signalized having significantly better results. The delay at a platoon depth of 9, for the unsignalized intersection, was -1.8, this brings the ideal delay to 98.2 seconds down from 100 seconds. This was due to the way intersection merging and gap threading is performed and is valid for several reasons. Because this method of intersection management desires to decrease the delay in traffic as much as possible, it would not make sense to slow down a platoon in order to perform a maneuver. The signalized method performed significantly worse than the un-signalized due to how the signalized method was implemented. Only one traffic light was green at a time, hence, whenever a platoon was stopped at intersection, that platoon would be guaranteed a delay of at most 56 seconds, which is how long a red light lasts for one signal. Though inefficient signal timing was used, we hypothesize that the behavior of a right of way system, such as a traffic light, is fundamentally flawed. By delegating the right of way to only a few lanes at a time, it is guaranteed that some vehicles will be stopped and will therefore experience delay. In order to show that the capacity of signalized intersection management through the use of traffic lights falls short when compared to un-signalized autonomous intersection management, better traffic light control methods will need to be tested in the future.}, author = {Alberto Heras and Lykes Claytor and Haris Volos and Hamed Asadi and Jonathan Sprinkle and Tamal Bose} }