Arnaud de La Fortelle

Analysis of reservation algorithms for cooperative planning at intersections

Intersections concentrate a too important number of accidents. It is quite obvious they are dangerous places because it is where cars potentially collide due to intersecting trajectories, as opposed to “normal” roads where they are parallel. This paper present a framework designed initially for cybercars (fully automated cars) but that could also be applied — though with major differences — to human driven cars. It is a world where vehicles have to reserve pieces of roads to cross a junction. This work is an enhancement of a previous work that demonstrated the feasibility of such a reservation algorithm.

Priority-based coordination of autonomous and legacy vehicles at intersection

Recently, researchers have proposed various intersection management techniques that enable autonomous vehicles to cross the intersection without traffic lights or stop signs. In particular, a priority-based coordination system with provable collision-free and deadlock-free features has been presented. In this paper, we extend the priority-based approach to support legacy vehicles without compromising above-mentioned features. We make the hypothesis that legacy vehicles are able to keep a safe distance from their leading vehicles. Then we explore some special configurations of system that ensures the safe crossing of legacy vehicles. We implement the extended system in a realistic traffic simulator SUMO. Simulations are performed to demonstrate the safety of the system.

Capacity-Aware Backpressure Traffic Signal Control

The control of a network of signalized intersections is considered. Previous work demonstrates that the so-called backpressure control provides stability guarantees, assuming infinite queues capacities. In this paper, we highlight the failing current of backpressure control under finite capacities by identifying sources of nonwork conservation and congestion propagation. We propose the use of a normalized pressure which guarantees work conservation and mitigates congestion propagation, while ensuring fairness at low traffic densities, and recovering original backpressure as capacities grow to infinity. This capacity-aware backpressure control enables improving performance as congestion increases, as indicated by simulation results, and keeps the key benefits of backpressure: the ability to be distributed over intersections and O(1) complexity.

Trajectory planning in a crossroads for a fleet of driverless vehicles

In the context of Intelligent Transportation Systems based on driverless vehicles, one important issue is the passing of a crossroads. This paper presents a supervised reservation system. Extending previous works in this direction, the proposed algorithm determines trajectories and speeds for all the vehicles willing to pass the intersection. The work is separated into tasks shared between the vehicles and an infrastructure in charge of the crossroads. This paper describes the characteristics of the algorithm, as well as considerations which led to it. Simulation results are given and support the argument that this algorithm is probably suboptimal but still performs well. Finally, this work shows there is place for ameliorations and hints to further improve the algorithm are given.

Priority-based intersection management with kinodynamic constraints

We consider the problem of coordinating a collection of robots at an intersection area taking into account dynamical constraints due to actuator limitations. We adopt the coordination space approach, which is standard in multiple robot motion planning. Assuming the priorities between robots are assigned in advance and the existence of a collision-free trajectory respecting those priorities, we propose a provably safe trajectory planner satisfying kinodynamic constraints. The algorithm is shown to run in real time and to return safe (collision-free) trajectories. Simulation results on synthetic data illustrate the benefits of the approach.

Back-pressure traffic signal control with unknown routing rates

The control of a network of signalized intersections is considered. Previous works proposed a feedback control belonging to the family of the so-called back-pressure controls that ensures provably maximum stability given pre-specified routing probabilities. However, this optimal back-pressure controller (BP*) requires routing rates and a measure of the number of vehicles queuing at a node for each possible routing decision. It is an idealistic assumption for our application since vehicles (going straight, turning left/right) are all gathered in the same lane apart from the proximity of the intersection and cameras can only give estimations of the aggregated queue length. In this paper, we present a back-pressure traffic signal controller (BP) that does not require routing rates, it requires only aggregated queue lengths estimation (without direction information) and loop detectors at the stop line for each possible direction. A theoretical result on the Lyapunov drift in heavy load conditions under BP control is provided and tends to indicate that BP should have good stability properties. Simulations confirm this and show that BP stabilizes the queuing network in a significant part of the capacity region.

Decentralized model predictive control for smooth coordination of automated vehicles at intersection

We consider the problem of coordinating a set of automated vehicles at an intersection with no traffic light. The priority-based coordination framework is adopted to separate the problem into a priority assignment problem and a vehicle control problem under fixed priorities. This framework ensures good properties like safety (collision-free trajectories, brake-safe control) and liveness (no gridlock). We propose a decentralized Model Predictive Control (MPC) approach where vehicles solve local optimization problems in parallel, ensuring them to cross the intersection smoothly. The proposed decentralized MPC scheme considers the requirements of efficiency, comfort and fuel economy and ensures the smooth behaviors of vehicles. Moreover, it maintains the system-wide safety property of the priority-based framework. Simulations are performed to illustrate the benefits of our approach.

State-Driven Priority Scheduling Mechanisms for Driverless Vehicles Approaching Intersections

Scheduling driverless vehicles with different priorities to pass through intersections efficiently and safely has been becoming an important passing-through intersection (PTI) problem in the field of novel intelligent traffic systems (ITS), which is increasingly becoming cyber-physical-fused and social-service-oriented. Considering new emerging features with possible priorities, a novel centralized priority scheduling mechanism is mainly explored in this paper. First, related pivotal aspects of environment and driverless vehicles are modeled by fusing their physical and kinematic characters. Based on these models, PTI-related motions are further abstracted as several reservation-oriented standard states and actions. Then, an event-triggered and state-driven autonomous control procedure is designed. By mapping vehicular relations in spatiotemporal domain into time-distance windows, a universal passing-through principle, rules, and priority-based scheduling mechanisms are proposed and described in detail. Finally, a priority scheduling algorithm sPriorFIFO is proposed and designed. These models and mechanisms are then implemented within an algorithm simulator, through which scheduling performances are verified and evaluated.

Large Deviations Rate Function for Polling Systems

In this paper, we identify the local rate function governing the sample path large deviation principle for a rescaled process n−1Qnt, where Qt represents the joint number of clients at time t in a polling system with N nodes, one server and Markovian routing. By the way, the large deviation principle is proved and the rate function is shown to have the form conjectured by Dupuis and Ellis. We introduce a so called empirical generator consisting of Qt and of two empirical measures associated with St, the position of the server at time t. One of the main step is to derive large deviations bounds for a localized version of the empirical generator. The analysis relies on a suitable change of measure and on a representation of fluid limits for polling systems. Finally, the rate function is solution of a meaningful convex program. The method seems to have a wide range of application including the famous Jackson networks, as shown at the end of this study. An example illustrates how this technique can be used to estimate stationary probability decay rate.

Analysis and Modeled Design of One State-Driven Autonomous Passing-Through Algorithm for Driverless Vehicles at Intersections

Autonomous passing-through intersections has been becoming one important research problem in the domain of intelligent traffic, especially with the real emerging of driverless vehicles. After analyzing this problem and related work, some pivotal aspects, including the lane, path, critical section and vehicle, are modeled with considering relations among their physical and kinetic characters. Then, we abstract some basic actions of this passing procedure, and propose a universal state-based action model. With this model, the procedure will be equal to the switching between these actions and their states. Further, we propose a new centralized scheduling algorithm that is reservation-oriented, and can guarantee the higher request to be responded preferentially. Finally, this algorithm is simulated and the results show that it can promote the traffic efficiency, especially for vehicles with high priority.