Overtaking Maneuvers Research Articles

Introducing right turn on red for cyclists—a before-after study on behavioural adaption in Germany

Right Turn On Red (RTOR) for cyclists is a low-cost and easy-to-implement improvement for cycling that is practised in several European countries and has been discussed for implementation in Germany. This study investigates the effects of introducing RTOR for cyclists based on video observations at 43 sites in nine German cities including all relevant types of cycling infrastructure (mixed traffic, cycle lanes, cycle tracks). Using a before-after approach, the study compares cyclist behaviour in terms of compliance with traffic rules and conflicts with other users. Overall, the introduction of RTOR legalised behaviour that had already been practised. The share of cyclists turning right on red, which was already high at 80% before the introduction of RTOR, increased to 93% with RTOR, and cyclists were more likely to comply with traffic rules and less likely to cycle on the pavement. Conflicts were mainly observed between right-turning cyclists and other cyclists as well as pedestrians. The number of conflicts increased after the introduction of RTOR while conflict criticality decreased. Cyclists gave more space to other users and obstructed them less with RTOR in place. The only exception to this were conflicts in the approach, where more close overtaking manoeuvres and wriggling through other users were observed. RTOR should therefore only be recommended if sufficient space is available or cyclists mainly turn right at an intersection. Based on the results of this study, recommendations for RTOR for cyclists have been introduced in the German Highway Code.

Automatic Overtaking Path Planning and Trajectory Tracking Control Based on Critical Safety Distance

The overtaking process for autonomous vehicles must prioritize both efficiency and safety, with safe distance being a crucial parameter. To address this, we propose an automatic overtaking path planning method based on minimal safe distance, ensuring both maneuvering efficiency and safety. This method combines the steady movement and comfort of the constant velocity offset model with the smoothness of the sine function model, creating a mixed-function model that is effective for planning lateral motion. For precise longitudinal motion planning, the overtaking process is divided into five stages, with each stage’s velocity and travel time calculated. To enhance the control system, the model predictive control (MPC) algorithm is applied, establishing a robust trajectory tracking control system for overtaking. Numerical simulation results demonstrate that the proposed overtaking path planning method can generate smooth and continuous paths. Under the MPC framework, the autonomous vehicle efficiently and safely performs automatic overtaking maneuvers, showcasing the method’s potential to improve the performance and reliability of autonomous driving systems.

Interactive visualizations for assessing car-to-bicycle overtaking manoeuvres

Interactive visualizations for assessing car-to-bicycle overtaking manoeuvres

Vehicle Safety Application through the Integration of Flood Detection and Safe Overtaking in Vehicular Communication

Road safety in Malaysia is a major concern due to frequent floods and accidents caused by overtaking. These issues result in significant injuries and losses. In this paper, we introduce a new system called the Safe Driving Tool (SDT). The SDT integrates a Flood Detection System (FDS) and a Vehicle Overtaking System (VOS) using Long-Range (LoRa) communication technology. The FDS continuously monitors water levels in flood-prone areas. It alerts drivers about potential hazards through vehicle-to-infrastructure (V2I) communication. Simultaneously, the VOS enables safe overtaking maneuvers. It does this by exchanging information with nearby vehicles through vehicle-to-vehicle (V2V) communication. Through testing and experimentation, we have shown that the SDT system effectively reduces accident risks and losses associated with floods and overtaking. The system's performance under various conditions confirms the reliability and effectiveness of LoRa communication technology in enhancing vehicular safety. This study represents a significant advancement in road safety. It combines flood detection and overtaking assistance into a single unified system, addressing two major causes of road accidents in Malaysia. The integration of V2I and V2V communication provides a comprehensive solution that improves driver awareness and decision-making. This ultimately leads to safer driving environments and enhanced driver convenience. Doi: 10.28991/CEJ-2024-010-09-015 Full Text: PDF

Overtaking on rural roads – Cyclists' and motorists' perspectives

Cycling is good for health and the environment, and urban traffic planning is increasingly focused on making cycling easier and safer for everyone. Cycling in rural areas also has a high potential for increased cycling, but fundamental knowledge about rural road cycling is missing. This study aims to investigate the prevalence of rural cycling and the perceptions of cyclists and motorists on rural roads, especially in overtaking situations. Cross-sectional data from three surveys with 1899 respondents in total were used, whereof one survey provides results representative for Swedish adults in terms of age, gender, income, educational level and geographical regions. In total, 38 % of the respondents use rural roads as cyclists or pedestrians, 10 % never use rural roads and the remaining 52 % use rural roads as motorists only. Out of the latter, 44 % stated that they would like to cycle on rural roads, but do not do so for various reasons. The way overtaking manoeuvres are experienced differs between motorists with and without cycling experience. Qualitative analyses revealed which factors make cyclists uncomfortable during overtaking manoeuvres on rural roads. These should be considered when developing recommendations for appropriate overtaking manoeuvres. Examples of application are legislation, motorist information campaigns, driver coaching and future vehicle automation systems.

Real-time simulation of ship overtake maneuvering at different water depths with a ship motion controller

This study aimed to predict ship trajectories in deep and shallow waters in real-time by properly accounting for the effects of hydrodynamic interactions on ship maneuvering. Ship maneuverability and motion control were investigated under hydrodynamic interactions arising from the effects of water depth, ship speed, and lateral distance between interacting ships on parallel courses. The Maneuvering Modeling Group (MMG) model was used, and the hydrodynamic interaction effect was estimated using an efficient three-dimensional potential-flow code that can run in real time. The influence of water depth on ship maneuverability was analyzed. The ship velocity response to the hydrodynamic interactions and maneuvering motion was simulated during the overtaking process of two ships at a range of water depths, lateral distances, and speeds. The findings suggest that, in shallow waters, changing the heading of the ship becomes more difficult, and the course deviation associated with the overtaking maneuver of two ships also increases. A conventional proportional-integral-derivative (PID) controller was employed for course-keeping. When the ratio of the water depth to ship draft was less than 2.0, the PID controller continued to perform effectively for the overtaking ship. For the overtaken ship, the effectiveness of the controller was diminished owing to the relatively low rudder effect at slow speeds.

Overtaking in Stuttgart—analysis of the lateral distances between motor vehicles and bicycle traffic with reference to traffic volume and cycling infrastructure

In the context of climate change, it is desirable to increase the share of cycling. One way of doing this can be to strengthen subjective safety of cyclists. At present, many people perceive cycling as unsafe. In particular, overtaking by motor vehicles is a cause of low subjective safety and stress. In built-up areas, German road traffic regulations stipulate a minimum lateral distance of 1.50 m for motor vehicles, while overtaking cyclists. Previous research has shown that this rule is often not followed by motor vehicles. The aim of this study is to find out which factors influence the lateral distance of overtaking manoeuvres. The lateral distances of 4 081 overtaking manoeuvres were recorded using an ultrasonic sensor on 14 selected routes in the city of Stuttgart, Germany. 42% of the recorded overtaking manoeuvres were carried out with a lateral distance of less than 1.50 m. The mean value of all overtaking manoeuvres was 1.59 m. On roads with mixed traffic, higher lateral distances occurred than on roads with cycle lanes. In Germany, the motor vehicle traffic volume on a road is a key criterion for planning cycling infrastructure. However, it is not possible to confirm an influence of the motor vehicle traffic volume on the occurring lateral distances. The time of day at which overtaking manoeuvres take place also seems to have no effect on lateral distances.

SaBi3d—A LiDAR Point Cloud Data Set of Car-to-Bicycle Overtaking Maneuvers

While cycling presents environmental benefits and promotes a healthy lifestyle, the risks associated with overtaking maneuvers by motorized vehicles represent a significant barrier for many potential cyclists. A large-scale analysis of overtaking maneuvers could inform traffic researchers and city planners how to reduce these risks by better understanding these maneuvers. Drawing from the fields of sensor-based cycling research and from LiDAR-based traffic data sets, this paper provides a step towards addressing these safety concerns by introducing the Salzburg Bicycle 3d (SaBi3d) data set, which consists of LiDAR point clouds capturing car-to-bicycle overtaking maneuvers. The data set, collected using a LiDAR-equipped bicycle, facilitates the detailed analysis of a large quantity of overtaking maneuvers without the need for manual annotation through enabling automatic labeling by a neural network. Additionally, a benchmark result for 3D object detection using a competitive neural network is provided as a baseline for future research. The SaBi3d data set is structured identically to the nuScenes data set, and therefore offers compatibility with numerous existing object detection systems. This work provides valuable resources for future researchers to better understand cycling infrastructure and mitigate risks, thus promoting cycling as a viable mode of transportation.

Safe-by-construction autonomous vehicle overtaking using control barrier functions and model predictive control

Ensuring safety for vehicle overtaking systems is one of the most fundamental and challenging tasks in autonomous driving. This task is particularly intricate when the vehicle must not only overtake its front vehicle safely but also consider the presence of potential opposing vehicles in the opposite lane that it will temporarily occupy. In order to tackle the overtaking task in such challenging scenarios, we introduce a novel integrated framework tailored for vehicle overtaking manoeuvres. Our approach integrates the theories of varying-level control barrier functions (CBF) and time-optimal model predictive control (MPC). The main feature of our proposed overtaking strategy is that it is safe-by-construction, which enables rigorous mathematical proof and validation of the safety guarantees. We show that the proposed framework is applicable when the opposing vehicle is either fully autonomous or driven by human drivers. To demonstrate our framework, we perform a set of simulations for overtaking scenarios under different settings. The simulation results show the superiority of our framework in the sense that it ensures collision-free and achieves better safety performance compared with the standard MPC-based approach without safety guarantees.

Are you in the mood to pass? A study on the interplay of psychological traits and traffic on young drivers’ overtaking behavior on two-lane, two-way highways

Overtaking maneuvers – the passing of a vehicle traveling in the same direction through use of an opposing lane – on two-lane, two-way highways have significant implications on safety and highway performance. This research examines the interplay between young drivers’ psychological traits and traffic variables to better understand overtaking behavior. A total of 111 licensed drivers between the ages of 18 and 28 (inclusive) successfully completed four unique traffic scenarios presented in random order. In all four scenarios, the lead vehicle was a truck for which the speed and passing gap size varied. A standardized self-report questionnaire captured demographic and psychological factors including impulsiveness, mindfulness, attitude towards driving and depression. Results revealed that, irrespective of the traffic scenario, the propensity to overtake a lead vehicle is associated with lower attitudes towards rule violations and speeding. In more difficult traffic scenarios, the effects of impulsiveness and mindfulness are more pronounced. Concerning moods, young drivers with higher depressive symptoms are more likely to overtake in the most dangerous scenarios. These findings can enhance driver education campaigns and targeted curricula for young drivers promoting safe driving to lessen the social and economic burdens of high-impact crashes.

Monovision End-to-End Dual-Lane Overtaking Network without Map Assistance

Overtaking on a dual-lane road with the presence of oncoming vehicles poses a considerable challenge in the field of autonomous driving. With the assistance of high-definition maps, autonomous vehicles can plan a relatively safe trajectory for executing overtaking maneuvers. However, the creation of high-definition maps requires extensive preparation, and in rural areas where dual two-lane roads are common, there is little pre-mapping to provide high-definition maps. This paper proposes an end-to-end model called OG-Net (Overtaking Guide Net), which accomplishes overtaking tasks without map generation or communication with other vehicles. OG-Net initially evaluates the likelihood of a successful overtaking maneuver before executing the necessary actions. It incorporates the derived probability value with a set of simple parameters and utilizes a Gaussian differential controller to determine the subsequent vehicle movements. The Gaussian differential controller effectively adapts a fixed geometric curve to various driving scenarios. Unlike conventional autonomous driving models, this approach employs uncomplicated parameters rather than RNN-series networks to integrate contextual information for overtaking guidance. Furthermore, this research curated a new end-to-end overtaking dataset, CarlaLanePass, comprising first-view image sequences, overtaking success rates, and real-time vehicle status during the overtaking process. Extensive experiments conducted on diverse road scenes using the Carla platform support the validity of our model in achieving successful overtaking maneuvers.

Adopting an HMI for overtaking assistance - Impact of distance display, advice, and guidance information on driver gaze and performance

Conveying supporting information by an in-vehicle human–machine interface (HMI) overtaking assistance system represents a promising approach to facilitate safe overtaking maneuvers. Although various researchers have attempted to examine the safety of systems, numerous HMI concepts are not validated and the impacts of systems on driver behavior following exposure to a connected environment remain unclear. Thus, the objective of this study is to assess the effectiveness of various HMI concepts and their effects on drivers' gaze behavior and driving performance in the connected environment. Three types of supporting information, namely distance, advice and guidance, were designed based on implicit and explicit concepts and displayed on the HMI. The guidance information provides more detailed maneuver instructions. A driving simulator experiment was conducted to imitate a connected environment and included an overtaking scenario on a two-lane highway under three distance gaps (short, middle and long) of oncoming vehicles and four HMI conditions including baseline (3*4). 39 participants (mean = 28.3 years, SD = 8.2 years) drove the simulator. Visual attention, driving maneuver, and subjective evaluation data were collected. The results indicated that the supporting information was valid in facilitating overtaking behavior compared without HMI, particularly under the middle distance gap scenario. Advice information was the most effective, required the least attention, exhibited the best performance, and was accepted by drivers. Detailed maneuver instructions were perceived as helpful, although no effect on acceptance was found. The findings of the study provide valuable insights into the development of user-friendly overtaking assistance systems.

Statistical Models of Interactions between Vehicles during Overtaking Maneuvers

A practical challenge facing the adoption of self-driving vehicles is the complex influence of the lateral dimension in vehicle traffic. This phenomenon has received little attention in the literature and few quantitative descriptions of interactions between vehicles are available for model validation. This paper proposes an analysis of the kinematic variables describing vehicle interactions on both axes during overtaking maneuvers using linear as well as nonlinear and nonparametric models based on real-world highway data. The principal findings are as follows: (a) a mutual influence between pairs of vehicles, especially at small lateral separation distances; (b) the higher the longitudinal velocity, the greater the lateral distances, no doubt to avoid collisions; and (c) lateral accelerations that tend to narrow lateral distance are associated with longitudinal accelerations that tend to widen it. These results are consistent across the different models applied and also with previous studies.

Overtake or not – A computer-based driving simulation experiment on drivers’ decisions during transitions in automated driving

The increase in automated driving changes the role of the human driver. Under SAE level 3 conditions, the driver has to be able to take back the driving task at any time. However, drivers are allowed to engage in non-driving related tasks during the automated drive, which can result in drivers being out-of-the-loop when they have to take back manual control. This raises the question of whether drivers are able to make safe decisions in such situations. In a computer-based driving simulation experiment, N = 37 participants drove in automated mode on a two-lane rural road and were distracted by a non-driving related task. Because of blocked sensors due to a slower preceding vehicle, the automated system asked the driver if an automated overtaking maneuver should be carried out or not. The selected maneuver was executed by the automated vehicle in automated mode. In a within-subject design with repeated measures, the time budget (with time pressure vs. without time pressure) and the criticality of the situation (TTC low vs. TTC middle vs. TTC high) were manipulated, and the overtaking decision, the reaction time, the certainty about the decision and the perceived criticality were measured. As a result, drivers decided more frequently and faster for the automated overtaking maneuver with time pressure. In addition, in more critical situations, drivers decided less frequently for the automated overtaking maneuver and took more time for the decision. The results provide insights into the decision-making of drivers in automated driving and can be used in the design of take-overs as well as in the development of automated driving systems to enhance traffic safety.

Game Theory-Based Decision-Making and Iterative Predictive Lateral Control for Cooperative Obstacle Avoidance of Guided Vehicle Platoon

This paper presents a systematic decision-making and lateral control framework to realize cooperative obstacle avoidance (OA) of the guided vehicle platoons safely and integrally in multiple scenarios. In the control framework, a centralized decision-making strategy is developed to determine optimal overtaking maneuver (i.e., OA) in the dynamic driving environment, and a distributed lateral controller with a safe driving corridor planner is designed for lateral reference trajectories tracking. The proposed decision-making strategy formulates the influence mechanism between the controlled platoon and potential surrounding vehicles based on an interactive behavior model. Moreover, a game theory-based algorithm is applied to calculate safe timing of overtaking and select efficient overtaking mode while avoiding the negative impact on the surrounding vehicles. For the distributed lateral control strategy, the control problem is solved by a linear time-varying model predictive control (LTV-MPC) algorithm so as to reduce the computational burden. Furthermore, to improve the accuracy of LTV-MPC algorithm in varying scenarios, a tailored iterative control logic is presented. The performance of the proposed framework is evaluated on the dSPACE hardware-in-loop platform. The results show that the proposed framework works well under various test scenarios involving multiple static and moving obstacles.

Bayesian Networks for the Driver Overtaking Assistance System on Two-lane Roads

Unsuccessful overtaking maneuvers on two-lane rural roads are one of the major causes of road accidents in the 21st century. The complexity of this maneuver merits the adoption of a thorough method for developing a proposed assistance system to prevent accidents and consequently reduce the high number of fatalities and the associated economic costs. This study aims to introduce an intelligent Driver Overtaking Assistance System (DOAS) to assist drivers in performing overtaking maneuvers safely. The study also will introduce a method to assess the impact of all the influential variables related to the driver, vehicle, traffic, road, and the surrounding environment. In momentary driving situations, the DOAS uses the communicated information via Hello beacon messages (HBM) and a set of input sensors to measure the possibility of overtaking the preceding vehicle(s) proactively by considering whether the distance gap to the oncoming vehicle is sufficient for overtaking. Besides, the proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity to acquire all relevant information regarding the ambient driving environment and the vehicles involved in the overtaking. To do this, DOAS uses a Bayesian Network (BN) to model overtaking maneuvers. The work presented shows high accuracy and promising results in aiding safe overtaking, with significant improvements to overtaking maneuvers on two-lane rural roads.

Calibration Measurements and Computational Models of Sensors Used in Autonomous Vehicles

Unsuccessful overtaking maneuvers on two-lane rural roads are one of the major causes of road accidents in the 21st century. The complexity of this maneuver merits the adoption of a thorough method for developing a proposed assistance system to prevent accidents and consequently reduce the high number of fatalities and the associated economic costs. This study aims to introduce an intelligent Driver Overtaking Assistance System (DOAS) to assist drivers in performing overtaking maneuvers safely. The study also will introduce a method to assess the impact of all the influential variables related to the driver, vehicle, traffic, road, and the surrounding environment. In momentary driving situations, the DOAS uses the communicated information via Hello beacon messages (HBM) and a set of input sensors to measure the possibility of overtaking the preceding vehicle(s) proactively by considering whether the distance gap to the oncoming vehicle is sufficient for overtaking. Besides, the proposed system is a vehicle-based safety system based on the collection of contextual information from the driving vicinity to acquire all relevant information regarding the ambient driving environment and the vehicles involved in the overtaking. To do this, DOAS uses a Bayesian Network (BN) to model overtaking maneuvers. The work presented shows high accuracy and promising results in aiding safe overtaking, with significant improvements to overtaking maneuvers on two-lane rural roads.

Multiphase Overtaking Maneuver Planning for Autonomous Ground Vehicles Via a Desensitized Trajectory Optimization Approach

This article studies the problem of trajectory optimization for autonomous ground vehicles with the consideration of irregularly placed on-road obstacles and multiple maneuver phases. By introducing a series of event sequences, a new multiphase constrained optimal control formulation is constructed to describe the automatic overtaking process. Although existing trajectory optimization techniques can be applied to address the constructed problem, they may suffer from poor or premature convergence issues due to the complexity of the mission formulation. Thus, to offer an effective alternative, a novel desensitized trajectory optimization method is designed and implemented to explore the optimal overtaking maneuver for the AGVs. The proposed method applies a double layer structure, where an enhanced intelligent optimization method is used in the outer layer such that the main inner optimization routine can be boosted by starting at a better reference solution. The algorithm convergence as well as the solution optimality conditions are theoretically analyzed. Numerical results are provided to illustrate the validity of the established formulation. Comparative case studies were executed to demonstrate the quality of the obtained solution and the enhanced performance of the proposed trajectory optimization method.

Pathways to more space. What factors influence the lateral distance of overtaking vehicles from cyclists?

In a field test, 4,217 overtaking manoeuvres (OM) were recorded with two measuring bicycles in the study area of Dresden and subsequently evaluated with regard to the lateral passing distances (LPD). The minimum distance recently anchored in the German road traffic regulations (StVO) was observed by only 53% of those car drivers overtaking. It is confirmed that narrow street sections favour narrow overtaking distances, especially if marked bicycle traffic guidance systems (protective strips) are installed there. Significantly lower LPD are observed in the case of oncoming traffic and a second vehicle overtaking in parallel, as well as in the case of LPD caused by heavy traffic. At night, larger LPD are observed. As a result, marked guidance on narrow street sections should be avoided. Wide bike lanes or protected bike lanes are recommended at wide facilities.

A Tube-MPC Approach to Autonomous Multi-Vehicle Racing on High-Speed Ovals

Autonomous vehicle racing has emerged as vibrant, innovative technology development, demonstration platform in recent years. Universities, companies demonstrate their achievements on various vehicles - from 1:10th to full-scale prototypes. One of those platforms is the Dallara AV-21, and the spec-vehicle for the Indy Autonomous Challenge. This paper outlines the robust model predictive control (MPC) concept used within the software stack of the TUM Autonomous Motorsport team. It is based on a simplified friction-limited point mass model and a set of low-level feedback controllers. The remaining model uncertainties are managed via introducing a constraint-tightening approach based on a Tube-MPC approach. In contrast to classical tracking controllers, the optimization problem is formulated to freely optimize the trajectory while staying within certain maximum deviations of the reference. This approach allows to rely on a coarse output of the trajectory planning approach while maintaining smoothness requirements in steering, throttle, and brake actuation. The paper highlights the advantages of the proposed robust reoptimization concept compared to pure tracking formulations. It showcases the performance compared to a classical LQR controller and an MPC, which utilizes a vehicle model with a more sophisticated tire model. The controller achieved a top speed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{265 kmh}^{-1}$</tex-math></inline-formula> and lateral accelerations up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\text{21 ms}}^{-2}$</tex-math></inline-formula> during a two-vehicle competition involving dynamic overtaking maneuvers on the Las Vegas Motor Speedway, a famous racetrack with turns banked up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20^{\circ }$</tex-math></inline-formula> .