Planning for autonomous vehicles : ridesharing and traffic control

This report address two problems that could drive the adoption of autonomous vehicles (AV) – shared autonomous vehicle routing (SAV) problem and the autonomous intersection management system (AIM) location problem. The SAV routing problem is finding the optimal SAV to passenger matching as well as the SAV route choice. Since widespread use of SAVs would have significant effects on traffic congestion, we develop a new tabu search heuristic for the SAV routing problem under the influence of traffic congestion. The algorithm aims to minimize traveler’s travel time. It considers several adjacent solutions by repeatedly swapping travelers between SAV routes. A nearest traveler neighborhood is defined to choose travelers to consider for the swap procedure. The Sioux Falls network is used to test the performance of the heuristic with varying demand and fleet sizes. The heuristic is found to produce encouraging results in reducing the total passenger travel time. A series of experiments are performed to understand the sensitivity of the heuristic to its parameters and the effects of congestion. The AIM location problem is the problem of optimally locating AIMs in a network so as to improve the experienced travel times in the network. Traditional traffic signals are inefficient in taking advantage of the benefits of AVs. Previous studies show that full adoption of AIMs in a network is not necessarily an improvement. This report aims to develop a framework which can be used to identify the intersections where an implementation of AIMs is beneficial. To do so, two models are proposed. First, a regression model is developed to classify intersections based on their performance. Second, the AIM location problem is formulated as an optimization problem and a genetic algorithm is developed to identify the optimal distribution of AIMs in a network. Both approaches are tested on the downtown Austin network and are compared for their performanceOperations Research and Industrial Engineerin

SeekNet: Improved Human Instance Segmentation via Reinforcement Learning Based Optimized Robot Relocation

Amodal recognition is the ability of the system to detect occluded objects. Most state-of-the-art Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passive prediction or embodied recognition approaches. However, these approaches suffer from challenges in real-world applications, such as dynamic objects. We propose SeekNet, an improved optimization method for amodal recognition through embodied visual recognition. Additionally, we implement SeekNet for social robots, where there are multiple interactions with crowded humans. Hence, we focus on occluded human detection & tracking and showcase the superiority of our algorithm over other baselines. We also experiment with SeekNet to improve the confidence of COVID-19 symptoms pre-screening algorithms using our efficient embodied recognition system