An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles

Altché, Florent; Qian, Xiangjun; Fortelle, Arnaud de La

doi:10.1109/tits.2017.2736532

Cited by 24 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…a junction), we use them for longer term planning of all vehicles on a much larger network (the airport surface). Also, to better tackle general airport layouts and vehicle geometries, we do not limit the shape of conflict regions to hexagons as in [2,3] or ellipsoids as in [30]. We can do this efficiently because, in contrast to [2,3], in our approach the shape of the conflict regions does not affect the number of disjunctions (or binary variables) in the optimization model.…”

Section: Problem Description and Literature Reviewmentioning

confidence: 99%

Scheduling Vehicles with Spatial Conflicts

Kloster

Mannino

Riise

et al. 2022

Transportation Science

View full text Add to dashboard Cite

When scheduling the movement of individual vehicles on a traffic network, one must ensure that they never get too close to one another. This is normally modelled by segmenting the network and forbidding two vehicles to occupy the same segment at the same time. This approximation is often insufficient or too restraining. This study develops and systematises the use of conflict regions to model spatial proximity constraints. By extending the classical disjunctive programming approach to job-shop scheduling problems, we demonstrate how conflict regions can be exploited to efficiently schedule the collective movements of a set of vehicles, in this case aircraft moving on an airport ground network. We also show how conflict regions can be used in the short-term control of vehicle speeds to avoid collisions and deadlocks. The overall approach was implemented in a software system for air traffic management at airports and successfully evaluated for scheduling and guiding airplanes during an extensive human in the loop simulation exercise for the Budapest airport. Through simulations, we also provide numerical results to assess the computational efficiency of our scheduling algorithm.

show abstract

Section: Problem Description and Literature Reviewmentioning

confidence: 99%

Scheduling Vehicles with Spatial Conflicts

Kloster

Mannino

Riise

et al. 2022

Transportation Science

View full text Add to dashboard Cite

show abstract

“…When the CPS is in the task execution stage, when the task is handed over to the CPS unit for execution, the perception unit will continuously perceive the task to generate decision data, and the agent will detect and predict whether the task will succeed or not. If the prediction task cannot succeed, it will transmit information to the agent to make adjustments until it succeeds [24][25].…”

Section: E Autonomous Control Strategymentioning

confidence: 99%

Autonomous Coordinated Control Strategy for Complex Process of Traffic Information Physical Fusion System Based on Big Data

Yan

Wang

2020

IEEE Access

View full text Add to dashboard Cite

In the era of big data, the global data is growing explosively. The huge growth rate makes data processing and storage difficult, especially in the field of transportation. Based on the above background, this paper aims to study the autonomous coordinated control strategy for the complex process of traffic information physical fusion system based on big data. In this paper, the information physical fusion system is applied to the modern transportation system, and it is used to realize the high integration of computation, communication and control. Realize the independent and coordinated control of the transportation system. This paper proposes an autonomous traffic management mechanism based on multi-agent CPS system. In view of the instability and untimely of the original control strategy, a new traffic optimization control strategy conflict reduction control strategy is proposed. In order to solve the complexity of traffic system, the generation method of CPS autonomous control strategy based on multi-agent is studied and analyzed. Through the evaluation and verification of the conflict reduction control strategy and the online simulation of the incremental data synchronization strategy, it can be seen that the inconsistency ratio curves of message quantity and byte transmission quantity are always kept at a relatively low level, 1% and 2%, respectively. During the whole experiment, the average number of inconsistent messages and byte transmission of the agent are ideally controlled at 1.2 messages / train and 0.5kb/train.

show abstract

“…The study also does not model the differences between AVs and HVs except for their communication capabilities. In [21,22] intersection management is investigated for "semi-autonomous" vehicles, i.e., vehicles that support control-input override and V2I communication. Safety is guaranteed by allowing intersection to override control inputs from human drivers when they would result in an unsafe or blocked situation, which presents difficulties in current practice as most present HVs does not support such functions.…”

Section: Related Workmentioning

confidence: 99%

Safe Intersection Management for Mixed Transportation Systems with Human-Driven and Autonomous Vehicles

Liu

Hsieh

2018

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

View full text Add to dashboard Cite

Most recent studies on establishing intersection safety focus on the situation where all vehicles are fully autonomous. However, currently most vehicles are human-driven and so we will need to transition through regimes featuring a varying proportion of human-driven vehicles ranging from 100% to 0% before realizing such a fully autonomous future -if ever. We will therefore need to address the safety of hybrid systems featuring an arbitrary mixture of human-driven and autonomous vehicles. In fact recent incidents involving autonomous vehicles have already highlighted the need to study the safety of autonomous vehicles co-existing with human-driven vehicles. Motivated by this we address the design of provably safe intersection management for mixed traffic consisting of a mix of human-driven vehicles (HVs) as well as autonomous vehicles (AVs).To analyze such mixed traffic, we model HVs as nearsighted and with relatively loose constraints, permitting worst-case behavior while AVs are considered as capable of following much tighter constraints. HVs are allowed freedom to change their speed at any time while AVs are only allowed to change their speed at the beginning of a time slot through a Model Predictive Controller (MPC). AVs are assumed to possess a shorter response time and stronger braking capability than HVs in collision avoidance. Moreover, AVs obtain the permissions of passing through the intersection through vehicle-to-infrastructure (V2I) communication, while HVs achieve the same objective by following traffic lights.Taking the above differences into consideration, we propose a provably safe intersection management for mixed traffic comprised of an MPC-based protocol for AVs, a permission assignment policy for AVs along with a coordination protocol for traffic lights. In order to respect the distinctiveness of HVs, the proposed protocol ensures that the traffic lights as well as the semantic meanings of their colors are consistent with current practice. A formal proof of safety of the system under the proposed combined protocol is provided.

show abstract

An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles

Cited by 24 publications

References 43 publications

Scheduling Vehicles with Spatial Conflicts

Scheduling Vehicles with Spatial Conflicts

Autonomous Coordinated Control Strategy for Complex Process of Traffic Information Physical Fusion System Based on Big Data

Safe Intersection Management for Mixed Transportation Systems with Human-Driven and Autonomous Vehicles

Contact Info

Product

Resources

About