This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease.

Remote driving, as a backup system for automated vehicles, can play a vital role in their commercialization. However, delay is one of the major challenges in the practical application of remote driving. It not only degrades the stability of remote driven vehicles (RDVs) but also introduces delayed driving feedback, such as motion cueing feedback, to remote drivers. This can result in an unpleasant driving experience. This study proposes a square root cubature Kalman filter-based predictor (SRCKP) to compensate for driving feedback delays in remote driving. The SRCKP reduces the limitations of both model-based and model-free predictors (MFPs). Additionally, this article presents an overshoot compensator to address the overshoot problem associated with traditional MFPs. Furthermore, a packet loss predictor (PLP) is designed to mitigate the influence of packet loss during data transmission. Both simulation and hardware-in-the-loop (HIL) experiments during comprehensive driving scenarios are conducted to verify the effectiveness and robustness of the proposed method. The findings indicate that, compared with MFPs, the SRCKP reduces the L₂-norm error by up to 81.2% in simulations and by up to 54.0% in HIL experiments for the best-case conditions.

In this work, we address the problem of charging coordination between electric trucks and charging stations. The problem arises from the tension between the trucks’ nontrivial charging times and the stations’ limited charging facilities. Our goal is to reduce the trucks’ waiting times at the stations while minimizing individual trucks’ operational costs. We propose a distributed coordination framework that relies on computation and communication between the stations and the trucks, and handles uncertainties in travel times and energy consumption. Within the framework, the stations assign a limited number of charging ports to trucks according to the first-come, first-served rule. In addition, each station constructs a waiting time forecast model based on its historical data and provides its estimated waiting times to trucks upon request. When approaching a station, a truck sends its arrival time and estimated arrival-time windows to the nearby station and the distant stations, respectively. The truck then receives the estimated waiting times from these stations in response, and updates its charging plan accordingly while accounting for travel uncertainties. We performed simulation studies for 1,000 trucks traversing the Swedish road network for 40 days, using realistic traffic data with travel uncertainties. The results show that our method reduces the average waiting time of the trucks by 46.1% compared to offline charging plans computed by the trucks without coordination and update, and by 33.8% compared to the coordination scheme assuming zero waiting times at distant stations.

In this note, we consider infinite horizon optimal control problems with deterministic systems. Since exact solutions to these problems are often intractable, we propose a parallel model predictive control (MPC) method that provides an approximate solution. Our method computes multiple lookahead minimization problems at each time, where each minimization may involve a different number of lookahead steps, and terminal cost and constraint. The policy computed via parallel MPC applies the first control of the lookahead minimization with the lowest cost. We show that the proposed method can harnesses the power of multiple computing units. Moreover, we prove that the policy computed via parallel MPC has better performance guarantee than that computed via the single lookahead minimization involved in parallel MPC.

Truck platooning, an autonomous driving technology, reduces fuel consumption and emissions by organizing heavy-duty vehicles (HDVs) into convoys. While single-carrier platooning is feasible, cross-carrier implementations present challenges due to privacy concerns between competing carriers and third parties. This paper presents a real-time, privacy-preserving coordination framework for cross-carrier platooning. The framework safeguards sensitive itinerary data against both peer carriers and third-party service providers. Secure multi-party computation techniques are employed to ensure that planning data remains private, while collaborative decision-making enables effective coordination without the need for a centralized third party. A distributed model predictive control approach dynamically updates truck plans at hubs to optimize platooning opportunities. The framework is evaluated through large-scale simulations using real-world-inspired data, demonstrating its practicality. Results indicate a minor reduction in cost-saving performance but no significant computational overhead from privacy-preserving mechanisms compared to predictive coordination with the third party, highlighting an effective balance between privacy and coordination effectiveness.

The safety of unprotected road-users is crucial in any urban traffic. Occlusions and blind spots in the field-of-view of a vehicle can lead to unsafe situations. In this work, a specific pedestrian-crossing scenario is considered with an occlusion in the ego-vehicle's field-of-view. A novel framework is presented to enhance situational awareness based on vehicle-to-everything (V2X) communication to share perception data between vehicle and roadside units. It leverages set-based estimation utilizing a computationally efficient algorithm, for which the pedestrian is guaranteed to be located in a constrained zonotope. The proposed method has been validated through both simulation and real experiments. The real experiments are carried out on a test track using Scania autonomous vehicles.

Emergency maneuvers for autonomous tractor-semitrailer combinations have been studied in previous works, but questions remain in order to demonstrate safety in all weather and road conditions. By formulating a lane change maneuver in different road conditions as a numerical optimal control problem, an upper performance bound can be established and subsequently used as a benchmark in the development of autonomous driving or advanced driver assistance-systems. The resulting optimal maneuver indicates that the time-optimality of a maneuver is strongly linked to the force direction of the trailer axle. Comparing the optimal lane change maneuvers with optimal straight line braking indicates that the decision of whether to brake or evade in emergency situations should be informed by the available tire-road friction. The results can be used to inform safe autonomous vehicle behaviors in low-friction conditions.

In the future, eventually, robots will become extremely widespread also in urban environments, and perhaps, us humans will need to learn how to interact and live with them. Social navigation accounts for the problem of having a safe and efficient navigation among objects and pedestrians, which can be considered as sentient road users and, for this reason, more special considerations need be taken into account when dealing with them. The goal of any social navigation software stack is to make the robotic agent behave as similarly as possible to a pedestrian, which is used to abide to many social rules that has learnt throughout all of their life. In this way, humans will not need to learn new "robotic" rules for navigating an environment: they would only need to apply the same rules that also robots will follow. Many social navigation approaches rely on sociological-psychological studies in which the pedestrian motion has been modeled in deep details. In this work a novel approach is presented, leveraging the predictivity of Model Predictive Control and the reactivity of Social Force Model, which will model the pedestrian motion.

This work conducts a systematic experimental evaluation of the state-of-the-art Reference Aware Model Predictive Controller (RA-MPC) for autonomous vehicles. The RA-MPC is a path-tracking controller, that maximizes tracking accuracy and comfort. The controller uses a kinematic vehicle model with a nonlinear curvature response table that adapts the steering response online to the vehicle and operating conditions. The adaptiveness and robustness of the controller are analyzed by evaluating the performance on a highway truck, loaded and empty mining trucks, and a city bus. Moreover, highway-like and city-like scenarios are performed using the exact same implementation and parameter settings for all vehicles. The controller and model adaption achieved a very good path tracking performance in all experiments, deviating at most 25 cm from the reference path.

In recent years, advancements in autonomous driving technologies have accelerated the commercialization of highly automated trucks. This shift away from human drivers raises concerns about the loss of critical functions, particularly in post-diagnosis decision-making, which relies on human inputs in the current practice. This paper outlines the current post-diagnosis decision-making process for human-driven trucks, drawing on insights from industry practitioners, and systematically identifies gaps between these practices and the requirements for highly automated trucks. We propose a comprehensive design of an intelligent decision support system (DSS) to address these gaps. The design includes conducting a system impact analysis to identify new stakeholders, proposing a new DSS architecture with review and learning functions, and concretizing various potentially effective decision-making models and information inputs. Using a real-world freight delivery scenario and a risk-based decision-making approach, we present a case study to instantiate the DSS design, including graphical user interface designs and a step-by-step use case scenario. This work aims to adapt post-diagnosis decision-making for automated trucks at both technological and managerial levels, thereby enhancing vehicle reliability and transport efficiency.