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 work, we present a small-scale testbed for evaluating the real-life performance of cellular V2X (C-V2X) applications on 5G cellular networks. Despite the growing interest and rapid technology development for V2X applications, researchers still struggle to prototype V2X applications with real wireless networks, hardware, and software in the loop in a controlled environment. To help alleviate this challenge, we present a testbed designed to accelerate development and evaluation of C-V2X applications on 5G cellular networks. By including a small-scale vehicle platform into the testbed design, we significantly reduce the time and effort required to test new C-V2X applications on 5G cellular networks. With a focus around the integration of small-scale vehicle platforms, we detail the design decisions behind the full software and hardware setup of commonly needed intelligent transport system agents (e.g. sensors, servers, vehicles). Moreover, to showcase the testbed’s capability to produce industrially-relevant, real world performance evaluations, we present an evaluation of a simple test case inspired from shared situational awareness. Finally, we discuss the upcoming use of the testbed for evaluating 5G cellular network-based shared situational awareness and other C-V2X applications.

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.

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 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 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$3.5\%$lower than the distributed policy and$8\%$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.

Truck platooning is a promising technology that enables trucks to travel in formations with small inter-vehicle distances for improved aerodynamics and fuel economy. The real-world transportation system includes a vast number of trucks owned by different fleet owners, for example, carriers. To fully exploit the benefits of platooning, efficient dispatching strategies that facilitate the platoon formations across fleets are required. This paper presents a distributed framework for addressing multi-fleet platoon coordination in large transportation networks, where each truck has a fixed route and aims to maximize its own fleet’s platooning profit by scheduling its waiting times at hubs. The waiting time scheduling problem of individual trucks is formulated as a distributed optimal control problem with continuous decision space and a reward function that takes non-zero values only at discrete points. By suitably discretizing the decision and state spaces, we show that the problem can be solved exactly by dynamic programming, without loss of optimality. Finally, a realistic simulation study is conducted over the Swedish road network with$5,000$trucks to evaluate the profit and efficiency of the approach. The simulation study shows that, compared to single-fleet platooning, multi-fleet platooning provided by our method achieves around$15$times higher monetary profit and increases the CO$_2$emission reductions from$0.4\%$to$5.5\%$. In addition, it shows that the developed approach can be carried out in real-time and thus is suitable for platoon coordination in large transportation systems.

We study the strategic interaction among vehicles in a non-cooperative platoon coordination game. Vehicles have predefined routes in a transportation network with a set of hubs where vehicles can wait for other vehicles to form platoons. Vehicles decide on their waiting times at hubs and the utility function of each vehicle includes both the benefit from platooning and the cost of waiting. We show that the platoon coordination game is a potential game when the travel times are either deterministic or stochastic, and the vehicles decide on their waiting times at the beginning of their journeys. We also propose two feedback solutions for the coordination problem when the travel times are stochastic and vehicles are allowed to update their strategies along their routes. The solutions are evaluated in a simulation study over the Swedish road network. It is shown that uncertainty in travel times affects the total benefit of platooning drastically and the benefit from platooning in the system increases significantly when utilizing feedback solutions.

One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to adjust the ego-vehicle speed appropriately to maintain a proper safety margin. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety.

This paper presents a path following controller for autonomous vehicles, making use of the linear time-varying model predictive control (LTV-MPC) framework. The controller takes into consideration control input rates and accelerations, not only to account for limitations in the steering dynamics, but also to provide a safe and comfortable ride while minimizing wear and tear of the vehicle components. Furthermore, it introduces a method to handle model references generated by motion planning algorithms that can consider different vehicle models from the controller. The proposed controller is verified by simulations and through experiments in a Scania construction truck, and is shown to have better performance than the state-of-the-art smooth and accurate MPC.

This article describes a system to facilitate dynamic en route formation of heavy-duty vehicle platoons with the goal of reducing fuel consumption. Safe vehicle platooning is a maturing technology that leverages modern sensor, control, and communication technology to automatically regulate the inter-vehicle distances. Truck platooning has been shown to reduce fuel consumption through slipstreaming by up to 10%; under realistic highway-driving conditions. To further benefit from this technology, a platoon coordinator is proposed, which interfaces with fleet management systems and suggests how platoons can be formed in a fuel-efficient manner over a large region. The coordinator frequently updates the plans to react to newly available information. This way, it requires a minimum of customization with respect to the logistic operations. We discuss the system architecture in detail and introduce important underlying methodological foundations. Plans are derived in computationally tractable stages optimizing fuel savings from platooning. The effectiveness of this approach is verified in a simulation study. It shows that the coordinated platooning system can improve over spontaneously occurring platooning even under the presence of disturbances. A real demonstrator has also been developed. We present data from an experiment in which three vehicles were coordinated to form a platoon on public highways under normal traffic conditions. It demonstrates the feasibility of coordinated en route platoon formation with current communication and on-board technology. Simulations and experiments support that the proposed system is technically feasible and a potential solution to the problem of using vehicle platooning in an operational context.

Controlling the longitudinal movement of heavy-duty vehicles based on optimal control can be a cost-efficient way of reducing their fuel consumption. Such controllers today mainly exist for vehicles in haulage applications, in which the velocity is allowed to deviate from a constant set-speed. For distribution vehicles, which is the focus of this paper, the desired and required velocity has large variations, which makes the situation more complex. This paper describes the implementation of an optimal controller in a real heavy-duty distribution vehicle. The optimal control problem is solved offline as a Mixed Integer Quadratic Program, which yields reference trajectories that are tracked online in the vehicle. Some important steps in the procedure of the implementation are, except for designing the controller: developing a positioning system for the test track where the experiments are performed, estimating the parameters of the resistive forces, and setting the velocity constraints. Simulations show a potential of 10% reduction in fuel consumption without increasing the trip time. Experiments are then performed in a Scania truck, with the optimal solution as reference for the existing cruise control functions in the vehicle. It is concluded that in order to verify the fuel savings experimentally, the low-level controllers in the vehicle must be modified such that the tracking error is decreased.

Fuel efficient driving patterns are well investigated for highway driving, but less so for applications with varying speed requirements, such as urban driving. In this paper, the driving mission of a heavy-duty vehicle in urban driving is formulated as an optimal control problem. The velocity of the vehicle is restricted to be within upper and lower constraints referred to as the driving corridor. The driving corridor is constructed from a test cycle with large variations in the speed profile, together with statistics from vehicles in real operation. The optimal control problem is first solved off-line using Pontryagin's maximum principle. A sensitivity analysis is performed in order to investigate how variations in the driving corridor influence the energy consumption of the optimal solution. The same problem is also solved using a model predictive controller with a receding horizon approach. Simulations are performed in order to investigate how the length of the control horizon influences the potential energy savings. Simulations on a test cycle with varying speed profile show that 7% energy can be saved without increasing the trip time or deviating from a normal driving pattern. A horizon length of 1000 m is sufficient to realize these savings by the model predictive controller. The vehicle model used in these simulations is extended to include regenerative braking in order to investigate its influence on the optimal control policy and the results.

Lattice-based motion planners are an established method to generate feasible motions for car-like vehicles. However, the solution paths can only reach a discretized approximation of the intended goal pose. Moreover, they can be optimal only with respect to the actions available to the planner, which can result in paths with excessive steering. These drawbacks have a negative impact when used in real systems. In this paper we address both drawbacks by integrating a steering method into a state-of-the-art lattice-based motion planner. Un- like previous approaches, in which path optimization happens in an a posteriori step after the planner has found a solution, we propose an interleaved execution of path planning and path optimization. The proposed approach can run in real-time and is implemented in a full-size autonomous truck, and we show experimentally that it is able to greatly improve the quality of the solutions provided by a lattice planner.

Cooperative automated driving is a promising development in reducing energy consumption and emissions, increasing road safety, and improving traffic flow. The Grand Cooperative Driving Challenge (GCDC) 2016 was an implementation oriented project with the aim to accelerate research and development in the field. This paper describes the development of the two vehicle systems with which KTH participated in GCDC 2016. It presents a reference system architecture for collaborative automated driving as well as its instantiation on two conceptually different vehicles: a Scania truck and the research concept vehicle, built at KTH. We describe the common system architecture, as well as the implementation of a selection of shared and individual system functionalities, such as V2X communication, localization, state estimation, and longitudinal and lateral control. We also present a novel approach to trajectory tracking control for a four-wheel steering vehicle using model predictive control and a novel method for achieving fair data age distribution in vehicular communications.

In this paper, a lateral controller is proposed for an over-actuated vehicle. The controller is formulated as a linear time-varying model predictive controller. The aim of the controller is to track a desired path smoothly, by making use of the vehicle crabbing capability (sideways movement) and minimizing the magnitude of curvature used. To do this, not only the error to the path is minimized, but also the error to the desired orientation and the control signals requests. The controller uses an extended kinematic model that takes into consideration the vehicle crabbing capability and is able to track not only kinematically feasible paths, but also plan and track over non-feasible discontinuous paths. Ackermann steering geometry is used to transform the control requests, curvature, and crabbing angle, to wheel angles. Finally, the controller performance is evaluated first by simulation and, after, by means of experimental tests on an over-actuated autonomous research vehicle.

In this paper, we propose a controller for smooth autonomous path following. The controller is formulated as an economic model predictive controller. The economic cost introduced in the objective function leads to a smooth driving, since we minimize the first and second derivatives of the curvature function (i.e., we encourage linear curvature profiles). Since the curvature in clothoids varies linearly with the path arc-length, we use the smoothness and comfort characteristics of clothoid-driving to obtain a compact and intuitive controller formulation. We enforce convergence of the controller to the reference path with soft constraints that avoid deviations from the reference path. Finally, we present real life experiments where the controller is deployed on a Scania construction truck that show that the proposed controller outperforms a pure-pursuit controller. Moreover, we detail how the few tuning parameters can affect the obtained solution in practice.

Freight transportation is of outmost importance in our society and is continuously increasing. At the same time, transporting goods on roads accounts for about 26% of the total energy consumption and 18% of all greenhouse gas emissions in the European Union. Despite the influence the transportation system has on our energy consumption and the environment, road transportation is mainly done by individual long-haulage trucks with no real-time coordination or global optimization. In this paper, we review how modern information and communication technology supports a cyber-physical transportation system architecture with an integrated logistic system coordinating fleets of trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save about 10% of their fuel consumption. Utilizing road grade information and vehicle-to-vehicle communication, a safe and fuel-optimized cooperative look-ahead control strategy is implemented on top of the existing cruise controller. By optimizing the interaction between vehicles and platoons of vehicles, it is shown that significant improvements can be achieved. An integrated transport planning and vehicle routing in the fleet management system allows both small and large fleet owners to benefit from the collaboration. A realistic case study with 200 heavy-duty vehicles performing transportation tasks in Sweden is described. Simulations show overall fuel savings at more than 5% thanks to coordinated platoon planning. It is also illustrated how well the proposed cooperative look-ahead controller for heavy-duty vehicle platoons manages to optimize the velocity profiles of the vehicles over a hilly segment of the considered road network.

Heavy-duty vehicles driving close behind each other, also known as platooning, experience a reduced aerodynamic drag, which reduces the overall fuel consumption up to 20% for the trailing vehicle. However, due to each vehicle being assigned with different transport missions (with different origins, destinations, and delivery times), platoons should be formed, split, and merged along the highways, and vehicles have to drive solo sometimes. In this paper, we study how two or more scattered vehicles can cooperate to form platoons in a fuel-efficient manner. We show that when forming platoons on the fly on the same route and not considering rerouting, the road topography has a negligible effect on the coordination decision. With this, we then formulate an optimization problem when coordinating two vehicles to form a platoon. We propose a coordination algorithm to form platoons of several vehicles that coordinates neighboring vehicles pairwise. Through a simulation study with detailed vehicle models and real road topography, it is shown that our approach yields significant fuel savings.

Heavy-duty vehicle (HDV) platooning is a mean to significantly reduce the fuel consumption for the trailing vehicle. By driving close to the vehicle in front, the air drag is reduced tremendously. Due to each HDV being assigned with different transport missions, platoons will need to be frequently formed, merged, and split. Driving on the road requires interaction with surrounding traffic and road users, which will influence how well a platoon can be formed. In this paper, we study how traffic may affect a merging maneuver of two HDVs trying to form a platoon. We simulate this for different traffic densities and for different HDV speeds. Even on moderate traffic density, a platoon merge could be delayed with 20 % compared to the ideal case with no traffic.

Vehicle platooning is important for heavy-duty vehicle manufacturers, due to the reduced aerodynamic drag for the follower vehicles, which gives an overall lower fuel consumption. Heavy-duty vehicle drivers are aware this fact and sometimes drive close to other heavy-duty vehicles. However, it is not currently well known how many vehicles are actually driving in such spontaneous platoons today. This paper studies the platooning rate of 1,800 heavy-duty vehicles by analyzing sparse vehicle position data from a region in Europe during one day. Map-matching and path-inference algorithms are used to determine which paths the vehicles took. The spontaneous platooning rate is found to be 1.2 %, which corresponds to a total fuel saving of 0.07% compared to if none of the vehicles were platooning. Furthermore, we introduce several virtual coordination schemes. We show that coordinations can increase the platooning rate and fuel saving with a factor of ten with minor adjustments from the current travel schedule. The platooning rate and fuel savings can be significantly greater if higher flexibility is allowed.

The first edition of the Grand Cooperative Driving Challenge (GCDC) was held in the Netherlands in May 2011. Nine international teams competed in urban and highway platooning scenarios with prototype vehicles using cooperative adaptive cruise control. Team Scoop, a collaboration between KTH Royal Institute of Technology, Stockholm, Sweden, and Scania CV AB, Södertälje, Sweden, participated at the GCDC with a Scania R-series tractor unit. This paper describes the development and design of Team Scoop's prototype system for the GCDC. In particular, we present considerations with regard to the system architecture, state estimation and sensor fusion, and the design and implementation of control algorithms, as well as implementation issues with regard to the wireless communication. The purpose of the paper is to give a broad overview of the different components that are needed to develop a cooperative driving system: from architectural design, workflow, and functional requirement descriptions to the specific implementation of algorithms for state estimation and control. The approach is more pragmatic than scientific; it collects a number of existing technologies and gives an implementation-oriented view of a cooperative vehicle. The main conclusion is that it is possible, with a modest effort, to design and implement a system that can function well in cooperation with other vehicles in realistic traffic scenarios.