Xiangjun Qian

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.

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.

Optimal trajectory planning for autonomous driving integrating logical constraints: An MIQP perspective

This paper considers the problem of optimal trajectory generation for autonomous driving under both continuous and logical constraints. Classical approaches based on continuous optimization formulate the trajectory generation problem as a nonlinear program, in which vehicle dynamics and obstacle avoidance requirements are enforced as nonlinear equality and inequality constraints. In general, gradient-based optimization methods are then used to find the optimal trajectory. However, these methods are ill-suited for logical constraints such as those raised by traffic rules, presence of obstacles and, more generally, to the existence of multiple maneuver variants. We propose a new formulation of the trajectory planning problem as a Mixed-Integer Quadratic Program. This formulation can be solved efficiently using widely available solvers, and the resulting trajectory is guaranteed to be globally optimal. We apply our framework to several scenarios that are still widely considered as challenging for autonomous driving, such as obstacle avoidance with multiple maneuver choices, overtaking with oncoming traffic or optimal lane-change decision making. Simulation results demonstrate the effectiveness of our approach and its real-time applicability.

A hierarchical Model Predictive Control framework for on-road formation control of autonomous vehicles

This paper presents an approach for the formation control of autonomous vehicles traversing along a multi-lane road with obstacles and traffic. A major challenge in this problem is a requirement for integrating individual vehicle behaviors such as lane-keeping and collision avoidance with a global formation maintenance behavior. We propose a hierarchical Model Predictive Control (MPC) approach. The desired formation is modeled as a virtual structure evolving curvilinearly along a centerline, and vehicle configurations are expressed as curvilinear relative longitudinal and lateral offsets from the virtual center. At high-level, the trajectory generation of the virtual center is achieved through an MPC framework, which allows various on-road driving constraints to be considered in the optimization. At low-level, a local MPC controller computes the vehicle inputs in order to track the desired trajectory, taking into account more personalized driving constraints. High-fidelity simulations show that the proposed approach drives vehicles to the desired formation while retains some freedom for individual vehicle behaviors.

Time-optimal coordination of mobile robots along specified paths

In this paper, we address the problem of time-optimal coordination of mobile robots under kinodynamic constraints along specified paths. We propose a novel approach based on time discretization that leads to a mixed-integer linear programming (MILP) formulation. This problem can be solved using general-purpose MILP solvers in a reasonable time, resulting in a resolution-optimal solution. Moreover, unlike previous work found in the literature, our formulation allows an exact linear modeling (up to the discretization resolution) of second-order dynamic constraints. Extensive simulations are performed to demonstrate the effectiveness of our approach.

Challenges for Automated Cooperative Driving: The AutoNet2030 Approach

Automated driving is expected to significantly contribute to future safe and efficient mobility. Whereas classical automated approaches solely consider the host vehicle, AutoNet2030 aims to investigate a cooperative approach where communication is used to build decentralized control systems, facilitate cooperative traffic flow optimization, and enhance perception. This chapter introduces the concepts and methodology of AutoNet2030 in order to contribute to a cost-optimized and widely deployable automated driving technology.

Motion planning for urban autonomous driving using Bézier curves and MPC

This paper presents a real-time motion planning scheme for urban autonomous driving that will be deployed as a basis for cooperative maneuvers defined in the European project AutoNet2030. We use a path-velocity decomposition approach to separate the motion planning problem into a path planning problem and a velocity planning problem. The path planner first generates a collision-free piecewise linear path and then uses quintic Bézier curves to smooth the path with C2 continuity. A derive-free optimization technique Subplex is used to further smooth the curvature of the path in a best-effort basis. The velocity planner generates an optimal velocity profile along the reference path using Model Predictive Control (MPC), taking into account user preferences and cooperative maneuver requirements. Simulation results are presented to validate the approach, with special focus on the flexibility, cooperative-awareness and efficiency of the algorithms.

Least restrictive and minimally deviating supervisor for Safe semi-autonomous driving at an intersection: An MIQP approach

Although significant progress has been made in the last few years towards cooperative and autonomous driving, the transition from human-driven to fully automated vehicles is expected to happen slowly. The question of semi-autonomous driving, where Advanced Driver Assistance Systems assist human drivers in their decisions, will therefore become increasingly important. In this paper, we consider the problem of safe intersection crossing for semi-autonomous vehicles with communication capacities. We design an intersection supervisor based on a mixed-integer quadratic programming approach which monitors the control inputs of each vehicle, and overrides those controls when necessary to ensure that all vehicles can navigate safely. Moreover, the solution control deviates minimally from the vehicles target inputs: overriding only occurs when it is strictly necessary, in which case the control is chosen as close as possible to the drivers intent. We theoretically prove that the supervisor needs only consider a finite future time horizon to ensure safety and deadlock avoidance over an infinite time horizon, and we demonstrate through simulation that this algorithm can work in real time. Additionally, unlike previous work, our formulation is suitable for complex intersection geometries with a high number of vehicles.

An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles

Before reaching full autonomy, vehicles will gradually be equipped with more and more advanced driver assistance systems (ADAS), effectively rendering them semi-autonomous. However, current ADAS technologies seem unable to handle complex traffic situations, notably when dealing with vehicles arriving from the sides, either at intersections or when merging on highways. The high rate of accidents in these settings proves that they constitute difficult driving situations. Moreover, intersections and merging lanes are often the source of important traffic congestion and, sometimes, deadlocks. In this paper, we propose a cooperative framework to safely coordinate semi-autonomous vehicles in such settings, removing the risk of collision or deadlocks while remaining compatible with human driving. More specifically, we present a supervised coordination scheme that overrides control inputs from human drivers when they would result in an unsafe or blocked situation. To avoid unnecessary intervention and remain compatible with human driving, overriding only occurs when collisions or deadlocks are imminent. In this case, safe overriding controls are chosen while ensuring they deviate minimally from those originally requested by the drivers. Simulation results based on a realistic physics simulator show that our approach is scalable to real-world scenarios, and computations can be performed in real time on a standard computer for up to a dozen simultaneous vehicles.