Approximately Orchestrated Routing and Transportation Analyzer: Large-scale traffic simulation for autonomous vehicles

Carlino, Dustin; Depinet, Mike; Khandelwal, Piyush; Stone, Peter

doi:10.1109/itsc.2012.6338701

Cited by 25 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our strategy for evaluating intersection auctions is then presented in Section IV-B. The results and discussion of the experiments follow in Section IV-C. A. Simulator overview AORTA [16], the Approximately Orchestrated Routing and Transportation Analyzer, is an open source, agent-based microscopic simulator, written in Scala. The simulator, auction implementation, and experimentation framework total merely 9,500 lines of code.…”

Section: Resultsmentioning

confidence: 99%

Auction-based autonomous intersection management

Carlino

Boyles

Stone

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

Self Cite

204

138

View full text Add to dashboard Cite

Abstract-Autonomous vehicles present new opportunities for addressing traffic congestion through flexible traffic control schemes. This paper explores the possibility that auctions could be run at each intersection to determine the order in which drivers perform conflicting movements. While such a scheme would be infeasible for human drivers, autonomous vehicles are capable of quickly and seamlessly bidding on behalf of human passengers. Specifically, this paper investigates applying autonomous vehicle auctions at traditional intersections using stop signs and traffic signals, as well as to autonomous reservation protocols. This paper also addresses the issue of fairness by having a benevolent system agent bid to maintain a reasonable travel time for drivers with low budgets. An implementation of the mechanism in a microscopic simulator is presented, and experiments on city-scale maps are performed.

show abstract

Section: Resultsmentioning

confidence: 99%

Auction-based autonomous intersection management

Carlino

Boyles

Stone

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

Self Cite

204

138

View full text Add to dashboard Cite

show abstract

“…As noted above, the connectedness of AVs has been investigated in signal control policies which resulted in significant reductions in delay [46][47][48]. Other studies tend to extend the reach of the existing dynamic traffic assignment models to somehow include AVs [71,74,75]. Despite current computational technologies and current modeling knowledge, the scale of micro-simulation is limited to a portion of a city and not the entire city.…”

Section: Av Navigation Model 14mentioning

confidence: 99%

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

et al. 2016

View full text Add to dashboard Cite

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connectedvehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.

show abstract

“…throughput [15], however, this would require a majority of autonomous vehicles in the system and special solutions for manually driven vehicles. Furthermore, adapting the infrastructure for autonomous vehicles will most likely be an option for highways and primary roads.…”

Section: Summary and Contributionsmentioning

confidence: 99%

Adaptive Supervision Online Learning for Vision Based Autonomous Systems

Öfjäll

2016

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

Driver assistance systems in modern cars now show clear steps towards autonomous driving and improvements are presented in a steady pace. The total number of sensors has also decreased from the vehicles of the initial darpa challenge, more resembling a pile of sensors with a car underneath. Still, anyone driving a teleoperated toy using a video link is a demonstration that a single camera provides enough information about the surrounding world.Most lane assist systems are developed for highway use and depend on visible lane markers. However, lane markers may not be visible due to snow or wear, and there are roads without lane markers. With a slightly different approach, autonomous road following can be obtained on almost any kind of road. Using realtime online machine learning, a human driver can demonstrate driving on a road type unknown to the system and after some training, the system can seamlessly take over. The demonstrator system presented in this work has shown capability of learning to follow different types of roads as well as learning to follow a person. The system is based solely on vision, mapping camera images directly to control signals.Such systems need the ability to handle multiple-hypothesis outputs as there may be several plausible options in similar situations. If there is an obstacle in the middle of the road, the obstacle can be avoided by going on either side. However, the average action, going straight ahead, is not a viable option. Similarly, at an intersection, the system should follow one road, not the average of all roads.To this end, an online machine learning framework is presented where inputs and outputs are represented using the channel representation. The learning system is structurally simple and computationally light, based on neuropsychological ideas presented by Donald Hebb over 60 years ago. Nonetheless the system has shown a capability to learn advanced tasks. Furthermore, the structure of the system permits a statistical interpretation where a non-parametric representation of the joint distribution of input and output is generated. Prediction generates the conditional distribution of the output, given the input.The statistical interpretation motivates the introduction of priors. In cases with multiple options, such as at intersections, a prior can select one mode in the multimodal distribution of possible actions. In addition to the ability to learn from demonstration, a possibility for immediate reinforcement feedback is presented. This allows for a system where the teacher can choose the most appropriate way of training the system, at any time and at her own discretion.The theoretical contributions include a deeper analysis of the channel representation. A geometrical analysis illustrates the cause of decoding bias commonly present in neurologically inspired representations, and measures to counteract it. Confidence values are analyzed and interpreted as evidence and coherence. Further, the use of the truncated cosine basis function is motivated.Finally, a sele...

show abstract

Approximately Orchestrated Routing and Transportation Analyzer: Large-scale traffic simulation for autonomous vehicles

Cited by 25 publications

References 13 publications

Auction-based autonomous intersection management

Auction-based autonomous intersection management

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

Adaptive Supervision Online Learning for Vision Based Autonomous Systems

Contact Info

Product

Resources

About