Lane-free traffic is a new concept proposed for the era of connected and automated vehicles (CAVs). In this system, vehicles are no longer restricted to traditional lanes, and any lateral location within the entire road boundaries is considered for navigation. In the current lane-based traffic system, roundabouts, characterized by wide lanes or no clear lane markings, allow vehicles to have more lateral movement freedom and thus offer an ideal setting to investigate how CAVs should behave in lane-free conditions. This study introduces a new approach to controlling CAVs in a lane-free urban environment using Deep Reinforcement Learning (DRL). This constitutes the first time DRL has been applied to help intelligent vehicles drive through urban roundabouts without the constraints of traditional lanes. By allowing vehicles to use the entire road space, the model aims to provide a comfortable and collision-free driving experience, enabling vehicles to maintain desired speeds. Our methodology involves developing a Deep Deterministic Policy Gradient (DDPG) based control strategy that enables CAVs to make dynamic, real-time decisions for efficient navigation, merging, and exiting. To test this approach, we simulated a real-world roundabout, already deploying a lane-free design. We applied our model to all CAVs driving under various traffic patterns in that environment. We also compared its performance to a two-dimensional control strategy based on the self-driven particle model for mixed traffic. The findings indicate that our DRL-employing autonomous vehicles are able to learn smooth driving policies and achieve target speeds, in addition to avoiding collisions and ensuring a comfortable experience for passengers.

This paper introduces SumoWare, an interface integrating state-of-the-art platforms, SUMO and Autoware, to enable realistic evaluation of autonomous vehicle (AV) behavior in diverse traffic scenarios. Addressing the critical need for flexible and reproducible testing environments, SumoWare establishes seamless bidirectional communication, ensuring precise synchronization between the two systems. Experimental results demonstrate strong spatiotemporal consistency between the platforms and, based on a case study, reveal the implications of default conservative AV behavior on traffic flow, including reduced capacity and earlier congestion. These findings highlight the pressing need to refine planning and control strategies to mitigate the overly cautious nature of AV systems and optimize their integration into real-world traffic.

As one part of encountering present and future mobility challenges, intelligent transportation systems (ITS) have been developed over the years. As the overall goal, such systems aim to improve traffic efficiency and safety. Being an integral part of ITS for the fulfillment of the safety objective, cooperative perception realized by Vehicle-to-Everything communication (V2X) is considered a main contributor to enhancing safety. In recent years, several studies have explored the potential of such systems in safety use cases. As for driving assistance and automation systems, a common way of testing cooperative driving systems relies on the scenario-based testing approach. However, a systematic strategy is necessary to identify scenarios covering the majority of relevant situations for such applications. For defining an extensive set of scenarios, this study comprehensively analyzes the data set of the German In-Depth Accident Study (GIDAS) from the perspective of V2X-Relevance. The proposed methodology shows a systematic approach to identify relevant accident types and derive generic scenarios. In addition, this approach offers the possibility of defining standard test cases for future regulations.

Deep reinforcement learning has shown promise in various engineering applications, including vehicular traffic control. The non-stationary nature of traffic, especially in the lane-free environment with more degrees of freedom in vehicle behaviors, poses challenges for decision-making since a wrong action might lead to a catastrophic failure. In this paper, we propose a novel driving strategy for Connected and Automated Vehicles (CAVs) based on a competitive Multi-Agent Deep Deterministic Policy Gradient approach. The developed multi-agent deep reinforcement learning algorithm creates a dynamic and non-stationary scenario, mirroring real-world traffic complexities and making trained agents more robust. The algorithm’s reward function is strategically and uniquely formulated to cover multiple vehicle control tasks, including maintaining desired speeds, overtaking, collision avoidance, and merging and diverging maneuvers. Moreover, additional considerations for both lateral and longitudinal passenger comfort and safety criteria are taken into account. We employed inter-vehicle forces, known as nudging and repulsive forces, to manage the maneuvers of CAVs in a lane-free traffic environment. The proposed driving algorithm is trained and evaluated on lane-free roads using the Simulation of Urban Mobility platform. Experimental results demonstrate the algorithm’s efficacy in achieving various defined objectives. These include laterally sorting vehicles based on their desired speeds with minimal deviation from those speeds. Additionally, the jerk and acceleration values remain within acceptable ranges. Overall, these promising results highlight the potential of the proposed approach to enhance safety and efficiency in autonomous driving within lane-free traffic environments.

The need for additional capacity in motorway networks during periods of high demand is unavoidable if congestion is to be prevented. Increasing capacity by building new roads is often infeasible, leaving operation-based traffic control measures as the primary approach to exploit the existing infrastructure. In this paper, the novel concept of dynamic lane configuration is introduced, which opens a new avenue in motorway traffic control that harnesses the infrastructure for traffic improvement. The lateral capacity of the existing motorway infrastructure is under-utilized due to lanes that are much wider than the vehicle’s width. Dynamic lane configuration suggests that while current wide lanes ensure safety during high-speed driving, lower speed limits can be actively imposed during times of high traffic demand, allowing the lane width to be reduced, thanks to the reduced required lateral gap between vehicles at lower longitudinal speeds. By narrowing the lanes prior to congestion, it is possible to reclaim wasted space and add lanes to the road, leading to a dynamic capacity increase during the operation. This dynamic infrastructure layout with demand-responsive lane configuration during operation bridges the traffic management and infrastructure design. A model-based optimal control approach is developed to model the dynamic lane configuration and to define the times and locations of changing lane configuration. The proposed approach is tested in a simulation environment on two different motorway networks, each with a different configuration and demand profile. The promising results indicate the potential of the proposed approach in congestion mitigation and reducing travel time.

Floating Car Observers (FCOs) are an innovative method to collect traffic data by deploying sensor-equipped vehicles to detect and locate other vehicles. We demonstrate that even a small penetration rate of FCOs can identify a significant amount of vehicles at a given intersection. This is achieved through the emulation of detection within a microscopic traffic simulation. Additionally, leveraging data from previous moments can enhance the detection of vehicles in the current frame. Our findings indicate that, with a 20-second observation window, it is possible to recover up to 20% of vehicles that are not visible by FCOs in the current timestep. To exploit this, we developed a data-driven strategy, utilizing sequences of Bird’s Eye View (BEV) representations of detected vehicles and deep learning models. This approach aims to bring currently undetected vehicles into view in the present moment, enhancing the currently detected vehicles. Results of different spatiotemporal architectures show that up to 41% of the vehicles can be recovered into the current timestep at their current position. This enhancement enriches the information initially available by the FCO, allowing an improved estimation of traffic states and metrics (e.g. density and queue length) for improved implementation of traffic management strategies. The code and dataset are available at: https://github.com/urbanAIthi/TFCO

Accurate and comprehensive Bird’s Eye View (BEV) semantic segmentation is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative perception has exceeded the detection capabilities of single-agent systems, prevalent camera-based algorithms in cooperative perception neglect valuable information derived from historical observations. This limitation becomes critical during sensor failures or communication issues as cooperative perception reverts to single-agent perception, leading to degraded performance and incomplete BEV segmentation maps. This paper introduces TempCoBEV, a temporal module designed to incorporate historical cues into current observations, thereby improving the quality and reliability of BEV map segmentations. We propose an importance-guided attention architecture to effectively integrate temporal information that prioritizes relevant properties for BEV map segmentation. TempCoBEV is an independent temporal module that seamlessly integrates into state-of-the-art camera-based cooperative perception models. We demonstrate through extensive experiments on the OPV2V dataset that TempCoBEV performs better than non-temporal models in predicting current and future BEV map segmentations, particularly in scenarios involving communication failures. We show the efficacy of TempCoBEV and its capability to integrate historical cues into the current BEV map, improving predictions under optimal communication conditions by up to 2% and under communication failures by up to 19%. The code is available at https://github.com/cvims/TempCoBEV.

Background: Driving in urban traffic requires advanced cognitive skills: perceiving all relevant traffic participants, anticipating their likely trajectories, deciding which action to take, and controlling the vehicle. The underlying perceptual and cognitive processes are subject to occasional failures, which can depend in a complex way on learned heuristics and the cognitive load. Collisions between motor vehicles and vulnerable road users (VRU) in urban traffic remain frequent and have severe consequences. In this article, we study the behavior of drivers of motor vehicles turning right who are required to yield to cyclists riding straight through an intersection. A key potential error process is failure to perceive the cyclist.Methods: We conducted a trial with n = 35 subjects on our closed test track including observations of perceptual actions and gaze control, subject to variations in cognitive load and other factors. The artificial environment of a closed test track and the constraints due to ethical requirements pose challenges to the interpretation of any empirical trial. The current paper focuses on the trial design and on quantification of measurement validity.Results: Summary statistics involving trial features were assessed. Most participants reported that they performed the visual task of checking for cyclists in a manner similar to their behavior in real traffic (whether or not cyclist interactions were expected). The spatial distributions of driver glances to perceive cyclists were evaluated.Conclusion: The realism in this trial despite laboratory conditions may be attributable to ingrained skills and habits of participants. Laboratory trials can help to identify root causes of cognitive errors and ultimately guide efficient and effective deployment of bicycle safety countermeasures.

Automated driving offers excellent opportunities for ecology, economy as well as society. Especially in urban intersections, there is a considerable margin for benefits in these sectors. This work takes a structured simulation approach to find answers on the benefit of additional information about surrounding objects of automated vehicles provided by roadside ITS stations to utilize collective perception. We are using advanced sub-microscopic 3D hardware in the loop simulation framework to generate data based on which we can achieve reliable conclusions. Our simulation data set, consisting of 400 simulation iterations, suggests that automated vehicles greatly benefit from collective perception. A lack of roadside ITS station support might lead to a disruptive impact of automated vehicles on the macroscopic traffic flow of urban road networks. If roadside collective perception is introduced to the problem, maneuver time is reduced, and traffic efficiency is increased by 19.8% on average.

In order to facilitate safe interactions between automated vehicles (AVs) and vulnerable road users (VRUs) such as bicyclists, we present a communication application for mobile devices that allows an AV or its passenger and a bicyclist to interact in certain traffic scenarios. At the intersection, the AV or its passenger can change the existing right-of-way rules to prioritise the ego-vehicle or the bicyclist. In a coupled driving simulator in which these two road users can interact, 16 proof-of-concept experiments are conducted. It is found that the perceived safety at conflict points can be increased through the use of the application. An investigation of the user data provides insights into the AV passengers’ decision types and duration in the scenarios studied. Moreover, the simulation results are used to revise and further develop the application concept.

The comparably low driving ranges of battery electric vehicles (BEV) cause time-consuming recharging stops if long distances have to be covered. Thus, navigation systems not only have to compute routes leading from the BEV’s current position to the destination, but also to plan recharging stops. This type of routing problem is often modeled as a constrained shortest path problem. The constraint ensures that the BEV does not run out of energy. In this paper, a de facto deterministic reformulation of this problem type is suggested, which allows handling uncertainty–particularly the risks resulting from imperfect energy consumption predictions. For this purpose, a certain part of the battery capacity is used as an energy buffer. Different approaches to dynamically optimize the size of this energy buffer in dependency of the expected level of uncertainty are proposed and a corresponding modification of a typical routing algorithm is described. Furthermore, a simulation study is conducted showing that the described framework allows keeping the probability to run out of energy close to zero (for the test settings: < 0.5%) as long as a suitable approach for defining the size of the energy buffer is applied.

Safe and reliable automated driving is one key towards the future of mobility. We use smart road side infrastructure, which increases the field of view of road users, to step-up safety and reliability. We validate our claims regarding safety and reliability by extensive use of virtual testing. In this article we show our hardware-in-the-loop AV Testing Framework. We use a real vehicle computer, which is running open source planning and control algorithms, to demonstrate our findings. This computer is integrated in our testing framework, where we conduct complex urban traffic simulations with varying traffic demands and different traffic situations. The Field of View of the tested Automated Vehicle is increased using additional sensors mounted in the infrastructure. With road side infrastructure support, the 180 virtual test drives on an urban intersection, show a better performance in traffic efficiency and driving comfort. The simulated vehicle did not get stuck or was involved in collisions at any time.

Platform-based large-scale journey planning of autonomous vehicles and context-sensitive route planning applications require new scalable approaches in order to work within an on-demand mobility service. In this work we present and test a machine learning-based approach for distance-based roundtrip planning in a Traveling Salesman Problem (TSP) setting. We introduce our applied Distance-Based Pointer Network (DBPN) algorithm which solves mini-batches of multiple symmetric and asymmetric 2D Euclidean TSPs. We provide our algorithm and test results for symmetric and asymmetric TSP distances, as present in real road and traffic networks. Subsequently, we compare our results with an industry standard routing solver OR-Tools. Here, we focus on solving comparably small TSP instances which commonly occur on our platform-based service. Our results show that compared to the State-of-the-Art methods such as the Coordinate-Based Pointer Network (CBPN) and OR-Tools, our approach solves asymmetric TSPs which cannot be solved by the CBPN approach. The results furthermore show that our approach achieves near-optimal results by a 5.9% mean absolute percentage error, compared to the OR-Tools solution. By solving 1000 TSPs, we show that our DBPN approach is approximately 27 times faster than the OR-Tools solver.

With the deployment of partially and highly automated vehicles, the automotive industry is greatly increasing its influence on road traffic. In order to ensure a positive influence of automated vehicles on traffic efficiency as well as traffic safety, simulations are broadly used for the development and testing of the required functions. Since these simulations are applied to evaluate the behavior of an automated driving function in the real world, an exact representation of the real world in the simulation is essential for the validity of the generated results. Therefore, there is a need for methods with which certain parameters of real-world situations may be quantified and applied to a simulation. In this work, we propose an approach to measure traffic flow and estimate the traffic state in a network based on extended floating car data. For this purpose, the data concerning the movement of the tracked vehicles is combined with the data regarding surrounding traffic gathered by the vehicles' sensors. The aim of this combination is to achieve an accurate traffic observation on urban as well as rural roads with a minimal number of test vehicles gathering the data. The application of the method to simulated traffic results in an accurate estimation of the traffic volume. The functionality is also demonstrated based on a limited sample of real-world test data.

The path towards safe and highly automated driving is still under development. Especially when it comes to safety aspects, industry and research struggle to reach the required standards. In this paper we suggest using vehicle to infrastructure communication to provide a new kind of safety fallback solution in case conventional automated driving systems fail. This proposal is verified in a Hardware in the Loop setup. A fault is injected to the motion planner of a connected automated vehicle. The vehicle's own computing platform fails to generate a trajectory. An emergency message is sent to the infrastructure which seamlessly takes over the motion planning task and provides a backup trajectory to the vehicle. The infrastructure's motion plan is processed by the vehicle's motion controller and guides the vehicle to a safe stopping position. The scenario is executed with our research vehicle driving in a parking lot on a virtual highway and stopping on the hard shoulder. Additionally, a virtual vehicle is placed in the setup, acting as an obstacle on the shoulder lane of the highway. The whole scenario is thoroughly tested and shows one possibility on how the infrastructure can contribute to a safe path towards highly automated driving.

When considering autonomous mobility on demand (AMoD) trends, it is probably safe to assume that they will have a large market share in the near future. In the introductory phases, users of current non-autonomous mobility on demand services such as ride-hailing and carsharing are expected to be among the first users of AMoD systems. The research presented in this paper aims to estimate fares for an AMoD system in this early stages based on rental and financial data provided by a free-floating CS provider. It demonstrates that an autonomous taxi (aTaxi) model requires less vehicles to serve the same demand resulting in the possibility to lower fares. In our model, user behavior is represented by defining three maximal waiting times and three monetary values reflecting their dissatisfaction in the case, where they cannot be served in due time. Two bipartite optimization problems for vehicle-to-user and relocation assignments build the core of the introduced aTaxi model. Fleet size and relocation parameters are chosen according to a utility function representing profit and opportunity costs of users not being served. We compute the reduction in fares to break-even with the current CS profit. Results of a case-study in Munich, Germany, indicate that one aTaxi can replace 2.8-3.7 CS vehicles. The aTaxi operator can therefore reduce fares by 29%-35% to achieve the same profit assuming the same cost structures as in free-floating CS.

In this paper existing map matching algorithms are combined and modified such, that the resulting algorithm is suitable for the implementation on the graphics processing unit (GPU). The map matching algorithm implemented on GPU consists of a geometrical and topological processing step, which provides high accuracy with high efficiency at the same time. An important building block of the implementation is the parallelization of the R*-tree search. An efficient implementation is achieved by high data parallelism and minimal divergence between execution blocks. The presented map matching algorithm performs better than available open source implementations.

While scooters, often powered by 2-stroke engines, are quite popular in the southern parts of Europe they are rather scarce in the rest of Europe. Over the last years, electric-powered two-wheelers debuted on European markets. Since this type of vehicle can considerably improve traffic, reduce shortage of parking, and cut local emissions in cities, its low distribution and propagation in Europe seems rather inconvenient. This article presents the results of an escooter pilot study conducted in Munich, Germany, in order to broaden the understanding of the low distribution and of the field of application, usage, and constraints this kind of vehicle is facing in a European city. Therefore this article gives a summary about the subject of low-powered two-wheelers. In order to analyze the gathered data, a method to minimize irregularities in completion of a trip diary is presented. Final results of this pilot study show that escooters are particularly utilizable for commuting and leisure trips and can thereby substitute up to 64% of average daily distances. Additionally, weather conditions as constraints for escooter usage are examined. Even with weather conditions prevailing in Central Europe, escooters can be used at 58% of days per year without weather adjusted gear.

In this paper, an integrated approach combining offline precomputation of optimal candidate paths with online path retrieval and dynamic adaptation is proposed for a dynamic navigation system in a centralized system architecture. Based on a static traffic data file, a partially disjoint candidate path set is constructed prior to the trip using a heuristic link weight increment method. This method satisfies reasonable path constraints that meet the drivers' preferences, as well as alternative path constraints, that limit the joint failure probability for candidate paths. The characteristics of the proposed algorithm are the following: 1) The response time for online navigation demand is nearly linear with network size and less dependent on system load; 2) the veracity of the pretrip route plan based on the static data file is improved by taking travel time reliability into account; and 3) system optimization can be approximated without sacrificing driver preferences. The algorithm is tested on randomly generated road networks, and the numerical results show the efficiency of the approach