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.

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.

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.

Electric trucks usually need to charge their batteries during long-range delivery missions, and the charging times are often nontrivial. As charging resources are limited, waiting times for some trucks can be prolonged at certain stations. To facilitate the efficient operation of electric trucks, we propose a distributed charging coordination framework. Within the scheme, the charging stations provide waiting estimates to incoming trucks upon request and assign charging ports according to the first-come, first-served rule. Based on the updated information, the individual trucks compute where and how long to charge whenever approaching a charging station in order to complete their delivery missions timely and cost-effectively. We perform empirical studies for trucks traveling over the Swedish road network and compare our scheme with the one where charging plans are computed offline, assuming unlimited charging facilities. It is shown that the proposed scheme outperforms the offline approach at the expense of little communication overhead.

Remote driving plays a vital role in coordinating automated vehicles in challenging situations. Data transmission latency, however, can cause several problems in remote driving. Firstly, it can degrade the performance of remote-controlled vehicles, evident in issues like lane-following deviation and vehicle stability. Additionally, the remote control tower's driving feedback is affected by delayed vehicle signals, leading to delayed driving experience. To address this, a model-free-based predictor is employed to compensate for the delay in remote driving. This approach does not require any dynamic model of the system and only needs tuning of two parameters to reduce communication delay. This study enhances the previous work by mitigating the amplitude of overshoot around peak points. It leverages the principle of the second-order derivative to predict the signal's peak time and uses it to address the predictor's overshoot issue. The effectiveness of the proposed method is validated using real car data from multiple participants in two scenarios, including Slalom and lane-following. Simulation results indicate that the proposed method can reduce prediction error by nearly 25% compared to previous works. Moreover, the solutions in this study are capable of managing not only delays in remote driving vehicles but also in traditional mechanical systems, such as CAN bus delays in conventional cars.

In this work, we propose an approach for ensuring the safety of vehicles passing through an intelligent intersection. There are many proposals for the design of intelligent intersections that introduce central decision-makers to intersections for enhancing the efficiency and safety of the vehicles. To guarantee the safety of such designs, we develop a safety framework for intersections based on temporal logic and reachability analysis. We start by specifying the required behavior for all the vehicles that need to pass through the intersection as linear temporal logic formula. Then, using temporal logic trees, we break down the linear temporal logic specification into a series of Hamilton-Jacobi reachability analyses in an automated fashion. By successfully constructing the temporal logic tree through reachability analysis, we verify the feasibility of the intersection specification. By taking this approach, we enable a safety framework that is able to automatically provide safety guarantees on new intersection behavior specifications. To evaluate our approach, we implement the framework on a simulated T-intersection, where we show that we can check and guarantee the safety of vehicles with potentially conflicting paths.

This paper studies the optimal hub-based platoon formation at hubs along a highway under decentralized, distributed, and centralized policies. Hubs are locations along highways where trucks can wait for other trucks to form platoons. A coordinator at each hub decides the departure time of trucks, and the released trucks from the hub will form platoons. The problem is cast as an optimization problem where the objective is to maximize the platooning reward. We first show that the optimal release policy in the decentralized case, where the hubs do not exchange information, is to release all trucks at the hub when the number of trucks exceeds a threshold computed by dynamic programming. We develop efficient approximate release policies for the dependent arrival case using this result. To study the value of information exchange among hubs on platoon formation, we next study the distributed and centralized platoon formation policies which require information exchange among hubs. To this end, we develop receding horizon solutions for the distributed and centralized platoon formation at hubs using the dynamic programming technique. Finally, we perform a simulation study over three hubs in northern Sweden. The profits of the decentralized policies are shown to be approximately <inline-formula> <tex-math notation="LaTeX">$3.5\%$</tex-math> </inline-formula> lower than the distributed policy and <inline-formula> <tex-math notation="LaTeX">$8\%$</tex-math> </inline-formula> lower than the centralized release policy. This observation suggests that decentralized policies are prominent solutions for hub-based platooning as they do not require information exchange among hubs and can achieve a similar performance compared with distributed and centralized policies.

This paper proposes an observer-based formation tracking control approach for multi-vehicle systems with second-order motion dynamics, assuming that vehicles' relative or global position and velocity measurements are unavailable. It is assumed that all vehicles are equipped with sensors capable of sensing the bearings relative to neighboring vehicles and only one leader vehicle has access to its global position. Each vehicle estimates its absolute position and velocity using relative bearing measurements and the estimates of neighboring vehicles received over a communication network. A distributed observer-based controller is designed, relying only on bearing and acceleration measurements. This work further explores the concept of the Bearing Persistently Exciting (BPE) formation by proposing new algorithms for bearing-based localization and state estimation of second-order systems in centralized and decentralized manners. It also examines conditions on the desired formation to guarantee the exponential stability of distributed observer-based formation tracking controllers. In support of our theoretical results, some simulation results are presented to illustrate the performance of the proposed observers as well as the observer-based tracking controllers.

This paper proposes a novel formation control design for safe platooning and merging of a group of vehicles in multi-lane road scenarios. Provided a leader vehicle is independently controlled, the objective is controlling the follower vehicles to drive in the desired lane with a constant desired distance behind the neighboring (preceding) vehicle while preventing collisions with both the neighboring vehicle and the road's edges. Inspired by the recent concept of constructive barrier feedback, the proposed controller for each follower vehicle is composed of two parts: one is the nominal controller that ensures its tracking of the neighboring vehicle; another is for collision avoidance by using divergent flow as a dissipative term, which slows down the relative velocity in the direction of the neighboring vehicle and road edges without compromising the nominal controller's performance. The key contribution of this work is that the proposed control method ensures collision-free platooning and merging control in multi-lane road scenarios with computational efficiency and systematic stability analysis. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms.

Vehicle-to-everything (V2X) communication holds significant promise for augmenting autonomous driving capabilities. Particularly in dense traffic with occluded areas, V2X can be used to share information about the respective observed areas between traffic participants. In turn, reducing uncertainty about unseen areas can lead to less conservative behaviors while maintaining collision avoidance.This paper aims to leverage V2X to improve situation awareness for trajectory planning. We particularly address two challenges: First, the ego vehicle may not always receive up-to-date information. Second, some areas may remain occluded despite receiving information from other participants.In this work, we fuse the received information about the detected free space. We use reachability analysis to compute areas that are guaranteed to be free despite being occluded. This way, we can maintain collision-avoidance guarantees. We demonstrate the benefits of our proposed method both in simulations and physical experiments.