Jonas Mårtensson

Vandana Narri, Jonah J. Glunt, Joshua A. Robbins et al.

Situational awareness for connected and automated vehicles describes the ability to perceive and predict the behavior of other road-users in the near surroundings. However, pedestrians can become occluded by vehicles or infrastructure, creating significant safety risks due to limited visibility. Vehicle-to-everything communication enables the sharing of perception data between connected road-users, allowing for a more comprehensive awareness. The main challenge is how to fuse perception data when measurements are inconsistent with the true locations of pedestrians. Inconsistent measurements can occur due to sensor noise, false positives, or unmodeled disturbances. This paper employs set-based estimation with constrained zonotopes to compute a confidence metric for the measurement set from each sensor. Estimated sets and their confidences are then fused using hybrid zonotopes. This method can account for inconsistent measurements, enabling reliable and robust fusion of the sensor data. The effectiveness of the proposed method is demonstrated in both simulation and real experiments.

Joas Kahlert, Ruiting Wang, Jonas Mårtensson

The electrification of long-haul freight transport introduces several new challenges, such as the limited capacity and congestion at en-route charging infrastructure. To reduce waiting times during peak periods, this paper proposes a framework for coordinated charging scheduling. The approach employs a mixed-integer formulation to optimize charging-related costs across charging, operation, battery degradation, and congestion delay, considering a range of scenarios. The results demonstrate that coordinated scheduling yields substantial cost savings up to 36% compared to uncoordinated scheduling, particularly by reducing battery degradation and delay costs.

Joas Kahlert, Ruiting Wang, Jonas Mårtensson

To support the adoption of electric transport systems, public charging opportunities are becoming increasingly important. In this dynamic environment, a central challenge for route planning and charging scheduling is forecasting charging-station availability under fluctuating demand. In this work, we propose a fluid-based forecasting method that accounts for uncertainty in both known and unforeseen electric vehicle arrival patterns while respecting station capacity constraints. We further evaluate the congestion forecasting method by applying it to an electric vehicle scheduling problem. Compared to scheduling frameworks that rely on standard baselines, charging schedules based on the fluid congestion forecasting model reduce waiting-related downtime by up to 14%. Finally, we quantify how increased knowledge of vehicle arrivals and different levels of station congestion affect overall system performance.

Xin Tao, Mladen Čičić, Jonas Mårtensson

With the development of vehicular technologies on automation, electrification, and digitalization, vehicles are becoming more intelligent while being exposed to more complex, uncertain, and frequently occurring faults. In this paper, we look into the maintenance planning of an operating vehicle under fault condition and formulate it as a multi-criteria decision-making problem. The maintenance decisions are generated by route searching in road networks and evaluated based on risk assessment considering the uncertainty of vehicle breakdowns. Particularly, we consider two criteria, namely the risk of public time loss and the risk of mission delay, representing the concerns of the public sector and the private sector, respectively. A public time loss model is developed to evaluate the traffic congestion caused by a vehicle breakdown and the corresponding towing process. The Pareto optimal set of non-dominated decisions is derived by evaluating the risk of the decisions. We demonstrate the relevance of the problem and the effectiveness of the proposed method by numerical experiments derived from real-world scenarios. The experiments show that neglecting the risk of vehicle breakdown on public roads can cause a high risk of public time loss in dense traffic flow. With the proposed method, alternate decisions can be derived to reduce the risks of public time loss significantly with a low increase in the risk of mission delay. This study aims at catalyzing public-private partnership through collaborative decision-making between the private sector and the public sector, thus archiving a more sustainable transportation system in the future.

Kleio Fragkedaki, Frank J. Jiang, Karl H. Johansson et al.

In this work, we present a transformer-based framework for predicting future pedestrian states based on clustered historical trajectory data. In previous studies, researchers propose enhancing pedestrian trajectory predictions by using manually crafted labels to categorize pedestrian behaviors and intentions. However, these approaches often only capture a limited range of pedestrian behaviors and introduce human bias into the predictions. To alleviate the dependency on manually crafted labels, we utilize a transformer encoder coupled with hierarchical density-based clustering to automatically identify diverse behavior patterns, and use these clusters in data-driven reachability analysis. By using a transformer-based approach, we seek to enhance the representation of pedestrian trajectories and uncover characteristics or features that are subsequently used to group trajectories into different "behavior" clusters. We show that these behavior clusters can be used with data-driven reachability analysis, yielding an end-to-end data-driven approach to predicting the future motion of pedestrians. We train and evaluate our approach on a real pedestrian dataset, showcasing its effectiveness in forecasting pedestrian movements.

Xin Tao, Jonas Mårtensson, Håkan Warnquist et al.

New autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator's profit. In this paper, we implement a vehicle health management system by addressing the maintenance planning issues of autonomous trucks on a transport mission. We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company. Both availability losses and maintenance costs are considered when evaluating the economic risk. We demonstrate the proposed model by numerical experiments illustrating real-world scenarios. In the experiments, compared to three baseline methods, the expected economic risk of the proposed method is reduced by up to $47\%$. We also conduct sensitivity analyses of different model parameters. The analyses show that the economic risk significantly decreases when the estimation accuracy of remaining useful life, the maximal allowed time of delivery delay before order cancellation, or the number of workshops increases. The experiment results contribute to identifying future research and development attentions of autonomous trucks from an economic perspective.

Vandana Narri, Amr Alanwar, Jonas Mårtensson et al.

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 appropriately adjust the ego-vehicle speed to maintain a proper safety margin. It is also argued that the framework is suitable for handling sensor failures and false detections in a systematic way. 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.

Rui Oliveira, Oskar Ljungqvist, Pedro F. Lima et al.

Driving heavy-duty vehicles, such as buses and tractor-trailer vehicles, is a difficult task in comparison to passenger cars. Most research on motion planning for autonomous vehicles has focused on passenger vehicles, and many unique challenges associated with heavy-duty vehicles remain open. However, recent works have started to tackle the particular difficulties related to on-road motion planning for buses and tractor-trailer vehicles using numerical optimization approaches. In this work, we propose a framework to design an optimization objective to be used in motion planners. Based on geometric derivations, the method finds the optimal trade-off between the conflicting objectives of centering different axles of the vehicle in the lane. For the buses, we consider the front and rear axles trade-off, whereas for articulated vehicles, we consider the tractor and trailer rear axles trade-off. Our results show that the proposed design strategy results in planned paths that considerably improve the behavior of heavy-duty vehicles by keeping the whole vehicle body in the center of the lane.

Rui Oliveira, Pedro F. Lima, Gonçalo Collares Pereira et al.

Driving in urban environments often presents difficult situations that require expert maneuvering of a vehicle. These situations become even more challenging when considering large vehicles, such as buses. We present a path planning framework that addresses the demanding driving task of buses in urban areas. The approach is formulated as an optimization problem using the road-aligned vehicle model. The road-aligned frame introduces a distortion on the vehicle body and obstacles, motivating the development of novel approximations that capture this distortion. These approximations allow for the formulation of safe and non-conservative collision avoidance constraints. Unlike other path planning approaches, our method exploits curbs and other sweepable regions, which a bus must often sweep over in order to manage certain maneuvers. Furthermore, it takes full advantage of the particular characteristics of buses, namely the overhangs, an elevated part of the vehicle chassis, that can sweep over curbs. Simulations are presented, showing the applicability and benefits of the proposed method.

Rui Oliveira, Pedro F. Lima, Marcello Cirillo et al.

We present a trajectory generation framework for control of wheeled vehicles under steering actuator constraints. The motivation is smooth autonomous driving of heavy vehicles. The key idea is to take into account rate, and additionally, torque limitations of the steering actuator directly. Previous methods only take into account curvature rate limitations, which deal indirectly with steering rate limitations. We propose the new concept of Sharpness Continuous curves, which uses cubic and sigmoid curvature trajectories together with circular arcs to steer the vehicle. The obtained trajectories are characterized by a smooth and continuously differentiable steering angle profile. These trajectories provide low-level controllers with reference signals which are easier to track, resulting in improved performance. The smoothness of the obtained steering profiles also results in increased passenger comfort. The method is characterized by a fast computation time, which can be further speeded up through the use of simple pre-computations. We detail possible path planning applications of the method, and conduct simulations that show its advantages and real time capabilities.