Safety assurance using all perceptual information to predict the motion of dynamic agents is critical in urban environments and remains an open challenge. For Autonomous Vehicles (AV) operating around vulnerable road users, the risk assessment strategy often needs to address stochastic uncertainties in the multiple possible trajectories (or multimodal motion) of the surrounding traffic agents. However, this increases the complexity of the navigation problem using the existing planners. To address this issue, this paper presents a multi-level optimization strategy that combines sampling-based and direct optimization methods for decision-making and control with improved safety and trajectory smoothness. In the primary stage, a sampling-based optimization framework systematically identifies safe candidate trajectories by employing the Fusion of stochastic Predictive Inter-Distance Profile (F-sPIDP). F-sPIDP encapsulates the multimodal dynamics of traffic agents and explicitly computes the uncertainties in their estimated or tracked states. From the set of trajectories, a reference optimal trajectory and its F-sPIDP setpoints are selected, adhering to stringent safety constraints and motion smoothness. Subsequently, a secondary local control optimization refines the optimal trajectory to ensure compliance with the AV's kinematic and dynamic constraints while accounting for the quantified uncertainty within the F-sPIDP framework. The performance of the proposed method was assessed through simulations and statistical analyses, evaluating its robustness to diverse levels of uncertainty.

Risk assessment and management in urban scenarios are difficult for automated vehicles due to perception uncertainties and the latent stochastic and high-dynamic behaviors of other agents e.g., Personal Light Electric Vehicles (PLEVs). Although the Predictive Inter-Distance Profile (PIDP) provides a continuous assessment of the risk between multiple agents, it fails when there are significant uncertainties in the estimated states of the agents. In this paper, we propose a Stochastic PIDP (sPIDP) to handle the uncertainties in the motion of the agents. sPIDP projects the uncertainties to the inter-distance between the agents. Furthermore, an Uncertainty-aware MPC is proposed to perform risk management. Statistical results considering multiple traffic scenarios show that our method is efficient for safe navigation.

In complex environments, Intelligent Vehicles (IVs) require reliable decision-making to ensure safe and efficient navigation. To guarantee the proper operation of the IV, it must operate within its Operational Design Domain (ODD) [1]. This means that, in a specific context, the vehicle must have the necessary capabilities to guarantee the efficient and robust working of its functions. Current decision-making approaches primarily address dynamic constraints but often fail to consider the full vehicle's ODD. When the ODD is considered, it is monitored to make only an immediate decision. For instance, if it starts raining, the vehicle decides to reduce speed and increase the following distance to ensure safe braking on wet roads. However, neglecting future long-term states could lead the vehicle to an imminent departure from its ODD, and this could increase the appearance of risky situations. This paper presents a decision-making architecture, focused on the tactical level, designed to address these gaps. By evaluating the vehicle's ODD across both current and future states, the proposed framework constructs a reachable horizon that supports long-term decision-making. The proposed architecture, called ODD-aptive, formalizes the decision-making process as a Markov Decision Process (MDP), enabling a systematic analysis of vehicle capabilities and reachable states confined to the vehicle's ODD. By focusing on long-term decision-making, this approach ensures IVs to remain functional, adaptable, and safe even under dynamic and evolving conditions, supporting a reliable autonomy of IVs.

Connected Autonomous Vehicles (CAVs) achieve efficient information sharing through V2V and V2I communication, enabling effective collaborative driving at unsignalized intersections and beyond. However, when unexpected issues such as communication blockages or failures occur, significant challenges occur to allow reliable and efficient cooperative decision-making. This paper introduces the Predicted Inter-Distance Profile based on Probabilistic Uncertainty Interval (PIDP-PUI) method and a cooperative optimized decision process under uncertain situations. Simulation results demonstrate that the proposed methods enable collision-free decision-making and efficient planning at unsignalized intersections, even under communication loss.

International audience

This paper proposes an Unsignalized Intersection Management Control Strategy (UIM-CS) to enable an autonomous vehicle to perform a safe and smooth maneuver, taking into account the curvilinear trajectories of the considered vehicles. This is done while using a metric to assess the risk of the encountered situation through the appropriate use of the Predicted Inter-Distance Profile (PIDP) [1], [2], and its controlled minimum (mPIDP). The proposed control is based on an adaptive PD controller where the parameters are learned by using an Adaptive Network based Fuzzy Inference System (ANFIS). The variables that allow the assessment of the dangerousness based on PIDP are carefully defined to allow the genericity of the approach to all types of insertions, especially the unsignalized one (e.g., roundabout or highway insertion) where the Autonomous Vehicle (called Ego-Vehicles (EVs) in what follows) has to make a choice on its behavior (acceleration/deceleration). The proposed approach for the creation of the dataset allowing the learning of the adaptive PD controller parameters, that directly influence the responsiveness of the EV while taking into account its actual capacity and constraints, is also presented. To demonstrate the reliability and safety of the overall proposed control architecture, several simulations are performed.

One of the most important and fundamental topics in autonomous navigated vehicle research is the lane change maneuver for obstacle avoidance or overtaking maneuver. In the literature, the lane change maneuver path for car-like vehicles has widely been generated with geometrically smooth segments by solving boundary conditions under given constraints. This paper proposes a new method of continuous curvature path generation for the issue of lane change maneuver for obstacle avoidance while solving the clothoids composition problem using an efficient algorithmic procedure. Conventional approaches resorting to mathematical or engineering optimization without considering human-side activity and response may fail to deliver driving performance that is favorable to humans. The novelty of the proposed method lies in its adoption of a human driving pattern, which is non-symmetric and composed of two different modes ofavoidanceandrecoveryduring the maneuver, and utilizes the property given by an appropriate iterative algorithm which takes into account all the constraints in order to solve the problem. As compared to conventional methods, the proposed method not only provides overall safety for obstacle avoidance, but also exhibits efficiency of increased comfort and human like steering motion during the lane change maneuver. The proposed path planning method is compared to other methods in order to validate its efficiency for safe and smooth obstacle avoidance maneuver.

Automated Driving System (ADS) requires a high fidelity decision-making strategy to palliate to uncertain environment and changing dynamics of other road users. Considering the uniqueness of each traffic situation, the task of modeling every use-case is nearly impossible. One solution is to verify the safety of the decided/planned maneuvers during the vehicle’s navigation. This will give ability to the system to re-plan and evade any dangerous situation. The main focus of this work relies on guaranteeing safety of the ADS in sudden hazardous and risky situation. In this aim, an evasive strategy is proposed as a part of an overall Probabilistic Multi-Controller Architecture (P-MCA) designed for safe automated driving under uncertainties. This P-MCA is composed of several complementary interconnected modules, and addresses thus the full pipeline from risk assessment, path planning to decision-making and control for an ADS. The evasive strategy relies on two identified steps. The first step is performed through the decision-making framework, where a Sequential Decision Networks for Maneuver Selection and Verification (SDN-MSV) calculates a discrete evasive action maneuver based on defined situational criteria. The second step consists in computing the corresponding low-level control. It is based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) that allows the ego-vehicle to pursue the advised collision-free evasive maneuver to avert an accident and to guarantee the vehicle’s safety at any time. The reliability and the flexibility of the overall proposed P-MCA and its elementary components have been validated in simulated traffic conditions, with various driving scenarios, and in real-time.

We deal here with a fleet of autonomous vehicles which is required to perform internal logistics tasks inside some protected area.This fleet is supposed to be ruled by a hierarchical supervision architecture, which, at the top level distributes and schedules Pick up and Delivery tasks, and, at the lowest level, ensures safety at the crossroads and controls the trajectories.We focus here on the top level, while introducing a time dependent estimation of the risk induced by the traversal of any arc at a given time.We set a model, state some structural results, and design, in order to route and schedule the vehicles according to a well-fitted compromise between speed and risk, a bi-level algorithm and a A* algorithm which both relies on a reinforcement learning scheme.

International audience

International audience

International audience

Being able to learn and transfer skills from one agent to another is a fundamental feature in constructing even more intelligent behaviors. In this paper, we introduce a new kind of architecture and information pipeline that aims to enable the transmission of skills from one robot to one or several others. The Universal Notice Network (UNN) originality lies in the fact that it clearly distinguishes knowledge necessary to solve the task from the agent intrinsic perceptions and capabilities, hence increasing its reusability and its potential transmission to other agents. In various experiments, focusing on manipulation and comanipulation tasks in original environments, we demonstrate the capabilities of the proposed method that takes advantage of reinforcement learning algorithms and domain knowledge, such as forward geometric model and inverse kinematics. In particular, we show that a learned UNN through the interactions of an agent with its environment is transmissible to other agents, conserving a similar perfomance level.

Guaranteeing the safety of an autonomous vehicle (AV) is a challenging task, especially if the perceived environment is highly uncertain and other road users deviate from their expected trajectories. In this paper, we propose a probabilistic overall strategy for risk assessment and management of AV in highway through a Sequential Level Bayesian Decision Network (SLBDN) and an appropriate analytical formalization of criteria for anomaly detection based on a Dynamic Predicted Inter-Distance Profile (DPIDP) between vehicles. Accordingly, the proposed system is designed to take the suitable maneuver decision, have a safety retrospection and verification over the current maneuver risk and take appropriate evasive action autonomously from moving obstacles. Moreover, this probabilistic framework accounts for measurements uncertainty through an Extended Kalman Filter (EKF) and for vehicles' maximum capacities. Since the proposed strategy has a short response time, integrating safety verification in the decision-making process makes real time evasive decisions possible. Several simulation results show the good performance of the overall proposed control architecture, mainly in terms of efficiency to handle probabilistic decision-making even for risky scenarios.

In this work, a novel interval-based/data-driven safety verification technique is introduced for Intelli-gent/Autonomous Vehicles (I/AV). The interval arithmetic is adopted to enhance the reliability of the analytical models used for the autonomous navigation. Furthermore, a data-driven technique, which monitors the correlation relating variables of the modeled system, is adopted to ameliorate the uncertainty assessment. In such a manner, tight bounds of safety margins are obtained. To provide reliable safety verification, the proposed risk management approach has been integrated on an Adaptive Cruise Control (ACC) system. It permits to detect erroneous uncertainty estimation of an Extended Kalman Filter (EKF). Simulation results prove the overall risk management efficiency and its ability to handle uncertainties.

In this paper, a multi-agent probabilistic optimization algorithm is applied to the problem of multi-vehicle coordination. The algorithm is known as “Probability Collectives” (PC) and has roots in Game Theory and Optimization theory. It is traditionally used for finding optimal solutions of NP-hard problems such as the travelling salesman problem. On the other end, the proposed PC formulation presented in this paper focuses on a minimal complexity implementation for solving the coordination problem in a time of the order of magnitude of 0.1. Besides time constraints, the emphasis in the design is put on ensuring that the algorithm always comes up with a feasible solution. Simulations show that both objectives are reached while having a decentralized algorithm, and flexible with respect to the type of situations it can deal with. Additional benefits of the PC algorithm include robustness to agent failure and the possibility to accommodate non-collaborative vehicles (market penetration of autonomous vehicles <; 100%).

This paper proposes a flexible multi-layer and multi-controller architecture for a dynamic navigation in the formation of a group of autonomous vehicles in constrained environments. The main objectives of this architecture are to ensure reliable navigation in the formation of the vehicles and to guarantee the stable and smooth reconfiguration of the fleet shape. A precise review and analysis of the main used leader-follower modeling for the control of a fleet of autonomous vehicles is conducted. After highlighting their advantages and drawbacks, an appropriate leader-follower approach based on deformable shape is proposed. At each sample time, the leader's state (pose and velocity), defined as the main dynamic target, is taken as a reference to guide the overall fleet dynamic. In addition, an analytic formulation of the maximum linear and angular velocities of the leader is proposed in order to guarantee the asymptotic stability of the navigation in formation as well as the fleet reconfiguration phases (between different formation shapes). An important focus of this paper corresponds to the proposition of a reliable strategy for the fleet reconfiguration, according to the environmental context (when, for instance, obstacles are detected). The safety of the fleet is formally demonstrated using an appropriate reconfiguration matrix, which takes into account the vehicles' set-points inter-distances to avoid any inter-vehicles collisions. In addition, an estimation of the formation parameters, according to an authorized minimum distance between the vehicles, is given. Simulations and experiments in different scenarios are performed to demonstrate the flexibility, reliability, and efficiency of the proposed dynamic navigation of a fleet of vehicles in formation.

This paper proposes an overall Multi-Controller Architecture (MCA) for safe and flexible navigation of autonomous navigation, under uncertainties in highway use-cases. In addition to the details given about the main modules (and their interactions) composing the proposed MCA, an important focus of the paper is made on the definition of a robust Two-Sequential Level Decision Network (TSLDN), which uses both: Extended Time-To-Collision (ETTC) metric and a new definition of a specific Predicted Inter-Distance Profile (PIDP, between vehicles during lane changes maneuvers) in order to estimate the maneuvers risks. The TSLDN is utilized for: the driving situation assessment, decision-making and for safety retrospection over the current maneuver risk. It allows us to have the best decision to achieve the vehicle navigation task while maximizing its safety. Several simulation results show the good performance of the overall proposed control architecture, mainly in terms of efficiency to handle probabilistic decision-making even for very risky scenarios.

This paper deals with the problem of unicycle mobile robot navigation in cluttered environments. It presents in particular an approach which permits to verify the stability of the control architecture of mobile robot using the reachability analysis. To perform this analysis, we consider the robot as a hybrid dynamic system. The latter is modeled by an hybrid automata in order to verify the reachability property by using the interval analysis. The simulation results validate the proposed control architecture.

International audience

This paper deals with reactive and flexible humanlike autonomous vehicle navigation. A human driver reactively guides his vehicle, performing a smooth trajectory within the roads limits until reaching the defined goal. To obtain a similar behavior with an unmanned ground vehicle (UGV), this paper proposes a flexible control law to drive a vehicle towards desired static or dynamic targets based on a novel definition of control variables and Lyapunov stability analysis. Moreover, a target assignment strategy, combined with an appropriate sigmoid function, that allow to perform smooth, flexible and safe vehicle navigation through successive waypoints is presented. The stability of the proposed control strategy is proved according to Lyapunov synthesis. Simulations and experiments are performed in different cases to demonstrate the reliability and efficiency of the control strategy.

This paper considers the navigation in formation of a mobile Multi-Robot System (MRS) in presence of obstacles. In such areas, the collision avoidance between the robots themselves and with other obstacles (static and dynamic) is a challenging issue. To deal with it, a reactive and a distributed control architecture is built. The navigation in formation of the MRS is ensured while tracking a global virtual structure (first controller). Limit-cycle principle is used to compute the setpoint of the obstacle avoidance task (second controller). In this paper, kinematic constraints of the robot are taken into account in order to generate an attainable set-point. The objective is to guarantee safety of the mobile robots with respect to their maximum velocities. Simulation and experimental results validate the proposed contributions.

In this paper, the control problem for a group of mobile robots keeping a geometric formation is considered. The proposed architecture of control allows to each robot to avoid obstacles and to rejoin the desired formation. To not complicate the control of such a system, it is proposed to divide the overall complex task into two basic tasks: attraction to a dynamical target, and obstacle avoidance. Thus, a desired geometric shape is defined and each robot has to track one node of this mobile shape. Each robot has to be autonomously able to avoid disturbing obstacles and to rejoin the formation in a reactive manner. Moreover, it chooses the optimal avoidance side thanks to limit-cycle method in order to reach as rapidly as possible its virtual target. The proposed control architecture is implemented in a distributed manner. In addition, this architecture uses the same control law (Lyapunov stable) for the two elementary tasks, and the switching from one task to another occurs only by changing the set-points. Experimental results validate the proposed control architecture.

This paper presents a hybrid control architecture based on subsumption and schemas motors principles in order to achieve complex and cooperative tasks. The control architecture implemented is constituted by a set of independent and elementary behaviors organized in layers of skills. Specific low-level behaviors, called altruistic behaviors and inspired by societies of insects (attractive or repulsive signals), are used to improve the efficiency of the control. Therefore, competitive and cooperative mechanisms are used in a unique hybrid architecture of control to perform a complex box-pushing task by a set of mini-robots. The analysis of an elevated number of simulations allows us to have statistical results (time to complete the task was chosen as performance criteria) which show the existence of an optimal number of robots to achieve the box-pushing task and underline the importance of the use of altruistic behaviors to enhance the cooperative task.