The vast majority of protocols for connected automated vehicles are based onstatussharing, i.e., communication of the current vehicle state among neighboring vehicles. Only recently the idea ofintentsharing has been put forward, where not only the current state, but also the vehicle intention in the near future can be communicated. In the context of Cooperative Adaptive Cruise Control (CACC), this work provides a system-theoretic framework for intent sharing through the lens of output regulation. We present analytical results showing two fundamental aspects of CACC with intent sharing: a) when vehicle-to-vehicle communication is reliable, intent sharing provides no benefits over status sharing, as both sharing paradigms result in the same protocol; b) intent sharing becomes beneficial when vehicle-to-vehicle communication is unreliable, in which case the latest communicated intent can be used to reconstruct the missing information of the neighboring vehicle in the near future. Together with theoretical analysis, numerical validations with synthetic and real-world data are provided, where the benefits of CACC with the proposed implementation of intent sharing are shown against several state-of-the-art CACC protocols.

Multi-modal vehicle trajectory prediction is crucial for autonomous driving in dynamic environments. Despite the significant progress in the field, the uncertainty and heterogeneity caused by the diversity in driving intentions and driving scenes still present major challenges to multi-modal prediction. Existing query-based prediction paradigms take into account the social context arising from the driving scene, but neglect priors regarding vehicle intentions that the historical trajectory may contain. We propose a Social-aware Water-wave Information Transmission Recurrent Acceleration Network, abbreviated as S-WITRAN, based on decoupling multi-modal prediction into an ego-aware and a social-aware learning stage. The ego-aware stage aims to relax the constraints from the driving scene to explore a diversity of future trajectory candidates. The social-aware stage aligns the candidates with respect to the social context arising from the driving scene. The information transmission is designed to extract from the vehicle’s historical trajectory priors about its possible intentions and dynamic states, which are integrated to form a multi-modal set of trajectories. Extensive experiments on the NGSIM, highD, and Argoverse2 datasets, as well as a benchmark evaluation in UniTraj, demonstrate the superior performance of the proposed model in both map-free and map-based datasets.

In longitudinal platooning, some key sources of uncertainty are the powertrain time constants of the vehicles. Because such time constants appear in the input matrix of the platooning dynamics, their correct estimation is either impractical with methods requiring persistence of excitation or impossible with methods requiring the input matrix to be known. This work proposes a novel adaptive longitudinal platooning method with correct estimation of the powertrain time constants. To achieve correct estimation, the composite adaptive control framework and its stability analysis are suitably modified to handle the time constant uncertainty in the design of the adaptive law. The result is a platooning protocol that guarantees convergence of the estimated time constants to their true values without the need for persistence of excitation: it is sufficient that the derivative of the acceleration is nonzero over a possibly short transient, an extremely relaxed excitation condition. Comparisons with state-of-the-art platooning solutions reveal advantages such as no required measurements of acceleration derivative or collection of past data. The robustness and practicality of the proposed design are also verified with CarSim-based platooning experiments.

Evidences have been provided that the effect of poorly designed vehicle automation systems may propagate from the single car up to the traffic dynamics. A reported example consists of car-following driver assistance systems triggering destabilizing group phenomena like phantom traffic jams and stop-and-go waves. It then becomes fundamental to study ‘group-oriented’ vehicle shared control algorithms that are able to assist the driver while at the same time prevent the propagation of destabilizing effects in the traffic when these systems are widely deployed. A key challenge in vehicle shared control is that the heterogeneity and uncertainty of human driving characteristics require personalized adaptation: it is an open problem to realize stabilizing traffic properties from personalized adaptive vehicle shared control. The distinguishing contribution of this work is a car-following shared control method that, while adapting to the personal characteristics of each driver, contains a ‘group’ model with desirable traffic properties defined in terms of string stability and collision avoidance. It is proven analytically that the proposed shared control is able to assist each driver in approaching the group model adaptively (i.e., handling heterogeneity and uncertainties) and optimally (i.e., with minimum control authority over the driver). Numerical experiments performed in SUMO with Highway Fuel Economy Test Cycle (HWFET) data and stop-and-go wave data validate that the proposed assistance improves traffic smoothing while handling heterogeneity and uncertainties in the driver parameters.

This work presents an adaptive open-source implementation of ArduPilot: the adaptive mechanisms in the autopilot are inspired by model reference stabilization (MRS) and are seamlessly embedded into the open-source ArduPilot suite. We illustrate MRS ArduPilot for the ArduPlane and ArduCopter modules (fixed-wing and rotary-wing vehicles): yet, the approach is general enough to be applicable to all aerial/surface/marine vehicles of ArduPilot, and even to PX4. Our tests show that the embedded adaptation makes the vehicle capable of handling uncertain scenarios like wind and varying payloads. The source code of MRS ArduPilot is released at https://github.com/Sunsun24/MRS.git

Cooperative adaptive cruise control (CACC) is one of the most studied platooning algorithms for connected vehicles. Despite its popularity, available studies neglect an important practical aspect of CACC: because the control input is a desired acceleration, existing CACC algorithms require an acceleration-hold loop, fed by accelerometer measurements that are noisy in practice. This work proposes new classes of CACC strategies that, while avoiding any feedback from the accelerometer, guarantee the same properties of existing designs. Theoretical properties are proven in terms of stability and string stability. Numerical tests, also performed in the open-source CARLA platooning toolbox named OpenCDA (cooperative driving automation), are provided to validate the theoretical properties of the design and the improved performance against noisy measurements.

Due to the use of inter-vehicle wireless communication, vehicular platooning can be prone to attacks with corrupted data, as in false data injection (FDI) attacks. It is crucial to develop platooning protocols promoting resilience to injected false data. In this work we show that resilience can be attained by making use of a system-theoretic property known as disturbance decoupling. We first show how disturbance decoupling is obtained in nominal platooning protocols without attacks: then, in the presence of FDI attacks, we propose compensation strategies that guarantee to recover the nominal performance of the platoon. The proposed compensation strategies can cope with platoons of heterogeneous vehicles and are designed towards string stability. Numerical experiments, also performed in a SUMO-Veins co-simulation environment with different platooning scenarios under FDI attacks, validate the effectiveness of the proposed protocols in handling cyber-attacks and platoon heterogeneity.

The interaction between automated and human-driven vehicles in mixed (human/automated) platoons is far from understood. To study this interaction, the notion of head-to-tail string stability was proposed in the literature. Head-to-tail string stability is an extension of the standard string stability concept where, instead of asking every vehicle to achieve string stability, a lack of string stability is allowed due to human drivers, provided it can be suitably compensated by automated vehicles sparsely inserted in the platoon. This work introduces a theoretical framework for the problem of head-to-tail string stability of mixed platoons: it discusses a suitable vehicle-following human driver model to study mixed platoons, and it gives a reduced-order design strategy for head-to-tail string stability only depending on three gains. The work further discusses the safety limitations of the head-to-tail string stability notion, and it shows that safety improvements can be attained by an appropriate reduced-order design strategy only depending on two additional gains. To validate the effectiveness of the design, linear and nonlinear simulations show that the string stability/safety trade-offs of the proposed reduced-order design are comparable with those resulting from full-order designs.

Switch-based adaptive dynamic programming (ADP) is an optimal control problem in which a cost must be minimized by switching among a family of dynamical modes. When the system dimension increases, the solution to switch-based ADP is made prohibitive by the exponentially increasing structure of the value function approximator and by the exponentially increasing modes. This technical correspondence proposes a distributed computational method for solving switch-based ADP. The method relies on partitioning the system into agents, each one dealing with a lower dimensional state and a few local modes. Each agent aims to minimize a local version of the global cost while avoiding that its local switching strategy has conflicts with the switching strategies of the neighboring agents. A heuristic algorithm based on the consensus dynamics and Nash equilibrium is proposed to avoid such conflicts. The effectiveness of the proposed method is verified via traffic and building test cases.

The broad learning system (BLS) paradigm has recently emerged as a computationally efficient approach to supervised learning. Its efficiency arises from a learning mechanism based on the method of least-squares. However, the need for storing and inverting large matrices can put the efficiency of such mechanism at risk in big-data scenarios. In this work, we propose a new implementation of BLS in which the need for storing and inverting large matrices is avoided. The distinguishing features of the designed learning mechanism are as follows: 1) the training process can balance between efficient usage of memory and required iterations (hybrid recursive learning) and 2) retraining is avoided when the network is expanded (incremental learning). It is shown that, while the proposed framework is equivalent to the standard BLS in terms of trained network weights,much larger networks than the standard BLS can be smoothly trained by the proposed solution, projecting BLS toward the big-data frontier.

A fast architecture for real-time (i.e., minute-based) training of a traffic predictor is studied, based on the so-called broad learning system (BLS) paradigm. The study uses various traffic datasets by the California Department of Transportation, and employs a variety of standard algorithms (LASSO regression, shallow and deep neural networks, stacked autoencoders, convolutional, and recurrent neural networks) for comparison purposes: all algorithms are implemented in MATLAB on the same computing platform. The study demonstrates a BLS training process two-three orders of magnitude faster (tens of seconds against tens-hundreds of thousands of seconds), allowing unprecedented real-time capabilities. Additional comparisons with the extreme learning machine architecture, a learning algorithm sharing some features with BLS, confirm the fast training of least-square training as compared to gradient training.

An accepted autopilot control architecture for fixed-wing unmanned aerial vehicles (UAVs) is the so-called cascaded loop closure, in which inner velocity loops and outer position loops are successively closed with proportional-integral-derivative (PID) controllers. This architecture has become so standard that popular open-source autopilots (e.g. ArduPilot, PX4) implement it in their codes. Despite its popularity, such architecture cannot adequately cope with the inevitable uncertainty in the UAV dynamics. In this work we present a "plug-and-play" adaptive module integrated in standard cascaded autopilot architectures, so as to can guarantee adaptation in the presence of uncertainty. The proposed module is analyzed and tested in a software-in-the-loop environment for an ArduPilot-based autopilot. The tests show that, in the presence of uncertainties occurring during flight, the proposed adaptation module outperforms the original autopilot as well as non-adaptive autopilots.

In adaptive platooning strategies proposed in literature to handle uncertain and nonidentical uncertain vehicle dynamics (uncertain heterogeneous platoons) two aspects requiring proper design are neglected: bidirectional interaction among vehicles which might lead to loss of string stability, and engine saturation constraints which might lead to loss of cohesiveness. This work proposes a novel adaptive platooning strategy handling these two crucial aspects. Specifically, bidirectional interaction is handled by designing bidirectional reference dynamics with proven string stability properties, to which the uncertain heterogeneous platoon should homogenize; engine constraints are handled via a proposed a mechanism that makes such reference dynamics `not too demanding', by properly saturating their action. The saturation action will allow all vehicles in the platoon to not hit their engine limits, preserving cohesiveness. Simulations are conducted to validate the theoretical analysis and show the effectiveness of the method in retaining cohesiveness of the platoon.

This work studies a new reinforcement learning method in the framework of Recursive Least-Squares Temporal Difference (RLS-TD). Differently from the standard mechanism of eligibility traces, leading to RLS-TD(λ), in this work we show that the forgetting factor commonly used in gradient-based estimation has a similar role to the mechanism of eligibility traces. We adopt an instrumental variable perspective to illustrate this point and we propose a new algorithm, namely - RLS-TD with forgetting factor (RLS-TD-f). We test the proposed algorithm in a Policy Iteration setting, i.e. when the performance of an initially stabilizing controller must be improved. We take the cart-pole benchmark as experimental platform: extensive experiments show that the proposed RLS-TD algorithm exhibits larger performance improvements in the largest portion of the state space.

The work presented in this paper concerns a switching-based control formulation for multi-intersection and multiphase traffic light systems. A macroscopic traffic flow modeling approach is first presented, which is instrumental to the development of a model-based and switching-based optimization method for traffic signal operation, in the framework of adaptive dynamic programming (ADP). The main advantage of the switching-based formulation is its capability to determine both “when”' to switch and “which” mode to switch on without the need to use the cycle-based average flow approximation typical of state-of-the-art formulations. In addition, the framework can handle different cycle times across intersections without the need for synchronization constraints and, moreover, minimum dwell-time constraints can be directly enforced to comply with minimum green/red times in each phase. The simulation experiments on a multi-intersection and multiphase traffic light systems are presented to show the effectiveness of the method.