Gabriel Rodrigues de Campos

Autonomous cooperative driving: A velocity-based negotiation approach for intersection crossing

In this article, a scenario where several vehicles have to coordinate among them in order to cross a traffic intersection is considered. In this case, the control problem relies on the optimization of a cost function while guaranteeing collision avoidance and the satisfaction of local constraints. A decentralized solution is proposed where vehicles sequentially solve local optimization problems allowing them to cross, in a safe way, the intersection. This approach pays a special attention to how the degrees of freedom that each vehicle disposes to avoid a potential collision can be quantified and led to an adequate formalism to the considered problem. In the proposed strategy, collision avoidance is enforced through local state constraints at given time instants and agents are assumed to only communicate the available time to react and the time stamps at which they expect to be within the intersection. Simulations results on the efficiency and performance of the proposed approach are also presented.

An approximate solution to the optimal coordination problem for autonomous vehicles at intersections

In this paper, we address the problem of optimal and safe coordination of autonomous vehicles through a traffic intersection. We state the problem as a finite time, constrained optimal control problem, a combinatorial optimization problem that is difficult to solve in real-time. A low complexity computational scheme is proposed, based on a hierarchical decomposition of the original optimal control formulation, where a central coordination problem is solved together with a number of local optimal control problems for each vehicle. We show how the proposed decomposition allows a reduction of the complexity of the central problem, provided that approximated parametric solutions of the local problems are available beforehand. We derive conditions for the construction of the parametric approximations and demonstrate the method with a numerical example.

Cooperative receding horizon conflict resolution at traffic intersections

In this article, we consider the problem of coordinating a number of vehicles crossing a traffic intersection. The proposed solution is based on a receding horizon formulation with a pre-defined decision order. In this approach, local problems are formulated for each vehicle, which are divided into a finite-time optimal control problem, where collision avoidance is enforced as terminal constraints, and an infinite horizon control problem, which can be solved offline. Feasibility conditions for a given decision sequence are also derived and simulation results are presented.

Challenges for cooperative ITS: Improving road safety through the integration of wireless communications, control, and positioning

For intelligent transportation systems (ITS) to achieve situational awareness beyond their sensing horizon and to harness coordination capabilities, some form of cooperation will be required. Such cooperation is enabled by vehicle-to-vehicle and vehicle-to-infrastructure wireless communication. The integration between the communication, signal processing, and control sub-systems is non-trivial and requires a co-design, which in turn requires collaboration between these three disciplines. This paper presents a possible evolution of these three disciplines within the context of ITS, as well as several challenges and opportunities.

Communication analysis for centralized intersection crossing coordination

Coordination of autonomous cooperative vehicles is an important challenge for future intelligent transportation systems. In particular, coordination to cross intersections captures the inherent and connected challenges among control and communication. While intersection coordination and vehicular wireless communication have both received extensive treatment in their respective communities, few works consider their interaction. We provide a communication system analysis for the specific problem of centralized intersection crossing coordination, leading to design guidelines for both uplink (whereby vehicles send intentions to the central controller) and downlink (where the controller prescribes vehicles of safe control actions).

Optimal and least restrictive supervisory control: Safety verification methods for human-driven vehicles at traffic intersections

We consider a cooperative conflict resolution problem at traffic intersections. Our goal is to design a least restrictive supervisor able to identify the optimal corrections to a human-decided input with respect to a given performance index, while keeping the system safe. Here, safety is formulated in terms of a maximal safe controlled invariant set. Leveraging results from scheduling theory, we characterize the preorder of the optimal solution set and propose an efficient optimization algorithm providing Pareto optimal solutions. We illustrate the application of the proposed algorithm through simulations in which vehicles crossing an intersection are optimally overridden by the supervisor only when necessary to maintain safety.

On the consensus of heterogeneous multi-agent systems: a decoupling approach

This article deals with consensus algorithms for heterogeneous multi-agent systems (MAS). A control strategy based on a consensus algorithm which is decoupled from the original systems is proposed. Consequently, its major advantage remains in the separation of the stability analysis of each subsystem and the distributed control algorithm. Through the paper, it is shown that our method allows using classical distributed consensus algorithms such as simple integrator consensus (with or without delay) and distributed consensus filter algorithms. Finally, some simulations supporting our theoretical results are presented, where the efficiency of the method is tested for completely arbitrary models.

Continuous-time double integrator consensus algorithms improved by an appropriate sampling

This paper deals with the double integrator consensus problem. The objective is the design of a new consensus algorithm for continuous-time multi-agent systems. The dynamic of agents is assumed to be of double integrator type. The proposed algorithm considers that there are no sensors to measure the velocity of the agents. Thus the classical double integrator consensus algorithm leads to an oscillatory behavior if the communication graph is undirected and to instability if the graph is directed. The novel algorithm proposes to sampled, in an appropriate manner, part of the multi-agent systems state such that the algorithm converges. An expression of the consensus equilibrium is provided. Some examples are provided to show the efficiency of the new algorithm.

Adaptive forward collision warning algorithm for automotive applications

This paper proposes an adaptive collision warning algorithm (CWA) that supports the driver by issuing early warnings for collision avoidance without noticeably increasing the risk of false alarms in real traffic. This algorithm can also detect when an emergency intervention is necessary. Compared to existing CWAs, the proposed solution in this paper triggers alarms by solving a linear convex program using traffic data, roads constraints and bicycle model dynamics, and by incorporating an adaptive (speed-dependent) warning threshold. A collision threat is detected by determining feasible steering trajectories without altering the vehicles longitudinal velocity.

Safety Verification Methods for Human-Driven Vehicles at Traffic Intersections: Optimal Driver-Adaptive Supervisory Control

We design an optimal, driver-adaptive supervisor for collision avoidance at an intersection. The algorithm is able to identify optimal corrections to the human-decided inputs and to keep the system collision free. To determine the set of safe control actions, we exploit the notion of maximal controlled invariant set. We leverage results from scheduling theory to verify the safety of a given control input, and propose an efficient optimization algorithm providing optimal solutions with respect to the drivers’ intent. We also present an approximate supervisor algorithm that can be solved in polynomial time and has guaranteed error bounds. Finally, we validate our approach with simulation results, as well as on naturalistic data.