Intelligent Speed Adaptation Research Articles

Intelligent speed adaptation for visibility technology affects drivers’ speed selection along curves with sight limitations

Sight obstructions along road curves can lead to a crash if the driver is not able to stop the vehicle in time. This is a particular issue along curves with limited available sight, where speed management is necessary to avoid unsafe situations (e.g., driving off the road or invading the other traffic lane). To solve this issue, we proposed a novel intelligent speed adaptation (ISA) system for visibility, called V-ISA (intelligent speed adaptation for visibility). It estimates the real-time safe speed limits based on the prevailing sight conditions. V-ISA comes with three variants with specific feedback modalities (1) visual and (2) auditory information, and (3) direct intervention to assume control over the vehicle speed.Here, we investigated the efficiency of each of the three V-ISA variants on driving speed choice and lateral behavioural response along road curves with limited and unsafe available sight distances, using a driving simulator. We also considered curve road geometry (curve direction: rightward vs. leftward). Sixty active drivers were recruited for the study. While half of them (experimental group) tested the three V-ISA variants (and a V-ISA off condition), the other half always drove with the V-ISA off (validation group). We used a linear mixed-effect model to evaluate the influence of V-ISA on driver behaviour.All V-ISA variants were efficient at reducing speeds at entrance points, with no discernible negative impact on driver lateral behaviour. On rightward curves, the V-ISA intervening variant appeared to be the most effective at adapting to sight limitations. Results of the current study implies that V-ISA might assist drivers to adjust their operating speed as per prevailing sight conditions and, consequently, establishes safer driving conditions.

Impact of federated deep learning on vehicle-based speed control in mixed traffic flows

This study investigates the application of Federated Learning (FL) in an environment of mixed traffic flow with Connected and Automated Vehicles (CAVs). The focus of this study is set on the CAV cruise control speed adjustment which is enforced by the Intelligent Speed Adaptation (ISA) system. It is modelled as an Actor-Critic-based learning architecture with the goal of computing the vehicle speed based on the data of the nearby vehicles. Some CAVs achieve poor travel time performance in comparison to others. Thus, the proposed FL framework selects periodically best-performing CAVs which are local clients in the context of FL. The updated global model is computed based on the weighted averaging of the best-performing models of clients. The use case environment is modelled as a simulation of a synthetic motorway with an induced capacity drop in the form of a work-zone under moderate and high traffic demand. It is shown that the FL framework can stabilise the individual client learning convergence and reduce their travel time compared to the local learning approach. The overall motorway throughput is also increased by the proposed FL framework. This indicates that the global model of FL can utilise the optimal ratio between generalisation and bias towards the most effective control policy of individual speed control. Results show that those effects are more prominent under moderate traffic demand.

Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris

PurposeThis article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.Design/methodology/approachRobots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.FindingsOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.Originality/valueOne of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

Towards the spatial analysis of motorway safety in the connected environment by using explainable deep learning

This study investigates the application of Connected and Automated Vehicles (CAVs) as moving sensors that transmit their speed and position in real-time for spatial analysis of motorway safety. Those data are used for the generation of image-alike inputs which describe the speed distribution over the entire motorway model in the form of heat-maps. Their labels are safety categories computed by using average Time-to-Collision (TTC). The Convolution Neural Network (CNN) is proposed to predict the category of safety based on the image-alike labeled dataset. Furthermore, Explainable Artificial Intelligence (xAI) is used to explain which segments of image-alike inputs are critical for the accurate prediction of safety. It is applied to selected inputs with the best learning performance and if they represent the undesirable safety categories. The study investigates the impact of various penetration rates of CAVs with the Intelligent Speed Adaptation (ISA) system on the spatial distribution of safety–critical regions. The higher penetration rates of the CAVs with the ISA system reduce the dispersion and intensity of critical regions computed by xAI over the entire motorway. Those regions are located at the most critical part of the analyzed motorway segment where the on-ramps flow interacts with the mainstream flow and its adjacent off-ramp. The higher penetration rate of CAVs with the ISA system induces a more consistent and localized distribution of critical regions regarding safety. Thus, this confirms that critical regions for safety categorization computed by xAI correspond with the motorway region with the most critical safety.

Adapting V ISA Technology: Driver Behaviour along Interchange Ramp and Merging/Diverging Terminals

An original in vehicle Intelligent Speed Adaptation System for Visibility (V-ISA) was designed to promote compliance with real-time speed management. The functionality of V-ISA is based on the prevailing sight conditions along the road, and operates in two variants: (i) providing visual information, and (ii) vehicle speed intervention. A road scenario with the driver performing merging and diverging manoeuvres in a motorway section was designed. A within-subject experiment involved 32 participants and a vehicle equipped with the two variants and the system off (baseline condition) under two traffic flow conditions. V ISA had a significant positive effect on the drivers’ longitudinal performance along ramps. This change in behaviour along ramps had no effect on merging and diverging manoeuvres. V ISA had a positive impact on driver speed behaviour in such complex road environment and assisted drivers to modulate the operating speed towards the safe dynamic limits.

A Novel, Energy-Efficient Smart Speed Adaptation Based on the Gini Coefficient in Autonomous Mobile Robots

Using energy efficiently is an important parameter in mobile robots. The majority of the energy consumption takes place in the motors. As such, past studies have investigated how to reduce the usage time of motors. Although the relationship between task energy and speed energy is considered in these studies, the qualification of the task, the amount of energy used, and the speed relation have not been taken into account as a whole. Parameters that affect each other in determining the speed profile, such as the criteria by which energy saving is determined, the maximum speed limit, acceleration, the load, and the ground relation, have not been taken into account holistically. Another research focus concerns the need to distribute energy in a balanced manner, in accordance with the qualification of the task, and to ensure the movement occurs at the optimum speed. In this study, a new dynamic (online) intelligent speed and acceleration adaptation method, based on the task structure and energy balance, was developed for a specific path that overcomes the shortcomings of existing models. The Gini coefficient was used for the balanced distribution of energy. Sharp speed changes were prevented with the remaining path and the balanced distribution of the remaining energy. The current model is compared with the trapezoidal speed profile structure.

How to take speed decisions consistent with the available sight distance using an intelligent speed adaptation system

Travelling at excessive speed increases the risk of having a road crash. Intelligent Speed Adaptation (ISA) systems might help the driver to make safe speed decisions along road sections with limited visibility. A recently developed ISA system, called V–ISA (Hazoor et al., 2021), is able to estimate the dynamic (real-time) speed limit, based on the prevailing sight conditions and stopping distance. The V-ISA operates in the following three ways: it can (i) display visual information, (ii) alert the driver with a warning sound, and/or (iii) intervene directly to modify and control vehicle speed. The effects of V-ISA on driving performance have yet to be investigated. Thus, the question of whether V-ISA modulates driving speed choice remains open. Here, we assessed the impact of V-ISA variants on driver speed choice. Thirty expert drivers experienced four simulated driving conditions, in which the three V–ISA variants together with the V-ISA off control condition were tested separately. Furthermore, drivers were asked for feedback on the acceptance and usability of the three V-ISA. Our results suggested that V–ISA was effective in mitigating the risks associated with speeding, with relatively high acceptance and perceived usability levels. The results indicate that V–ISA can have a positive impact on road safety by helping drivers to modulate their chosen driving speed.

Speed Limit Compliance Index (SLCI): A Conceptual Method to Enhance the Efficiency of the Advisory Intelligent Speed Adaptation System

Globally, as many as 50 million road users are injured in road traffic crashes and about 1.35 million of them die. Between 2002 and 2030, global deaths resulting from injuries caused by road traffic accidents are predicted to increase by over 40%. Speeding has a relatively eminent relationship with accident involvement and severity. The experiences of pioneers have revealed that the intelligent speed adaptation (ISA) system reasonably has brought a promising future to speed management and road safety. The findings from the earlier studies disclose that the effect of the advisory ISA on drivers’ behavior, particularly their driving speed choice, is positive and that it is the most desirable system among the drivers. Nevertheless, the system does not have a long-lasting effect, and when the system is removed or deactivated, the effect gradually disappears. Speed limit compliance index (SLCI) is a conceptual method in the form of a novel performance-based indicator and a mathematical formulation that would be potentially a feasible solution to address the abovementioned defect, and it could be an effective countermeasure to improve the efficiency of such a system. This study mainly aims to discuss and illustrate the structure of this method.

Impact of Deep Reinforcement Learning on Variable Speed Limit strategies in connected vehicles environments

The Variable Speed Limit (VSL) control is considered in the context of connected vehicles acting as moving sensors, while their obedience to speed limit is enforced by a mandatory Intelligent Speed Adaptation (ISA) system. The objective of this study is to extend spatially static differential VSL control by introducing novel VSL strategies that are based on spatially dynamic speed limit zones. The spatial configuration of speed limit zones requires a novel traffic state representation based on the set of consecutive matrices which encode each vehicle position and speed within the controlled motorway during the control time step. The actions for all proposed VSL strategies are computed by the same Deep Reinforcement Learning (DRL) approach based on the Deep Deterministic Policy Gradient (DDPG) architecture. The DDPG learning models contain integration of Convolution and Long Short-Term Memory (LSTM) known as ConvLSTM layers, along with the Convolution and Fully Connected layers. Thus, those models can learn complex spatio-temporal traffic dynamics based on proposed traffic state representation. The results show that proposed VSL strategies have achieved higher overall motorway throughput compared to one which is based on the static speed limit zones and baseline cases (no-control and Simple Proportional Speed Controller algorithm). Simultaneously, they achieved a minimal increase in the number of aggressive braking, while the average headway is increased. All proposed VSL control approaches are simulated and analyzed by using a synthetic microscopic motorway model and characteristic traffic scenario for urban motorways.

Describing I2V Communication in Scenarios for Simulation-Based Safety Assessment of Truck Platooning

V2X communication plays an important role in the transition towards connected, cooperative, automated driving. Wireless communication enables instant information exchange between vehicles (V2V) to support, e.g., platooning, and between the infrastructure and vehicles (I2V) to inform vehicles on, e.g., the local speed limit information or the approach of an accident location. In the Horizon 2020 HEADSTART project, we have shown how to test V2V communication in a scenario-based safety assessment framework. Safety assessment aims to determine the impact on safety in the case of potentially critical scenarios, e.g., due to, or in parallel to deterioration of communication. In this study, we extend this methodology with the incorporation of I2V communication. The developed method allows us to treat V2V and I2V communication independently. We demonstrate the method in the use case of an Intelligent Speed Adaptation I2V-functionality for platooning trucks. The practical implementation of test descriptions that consider the potential deterioration of communication signals in the standardized OpenSCENARIO format is shown. The study illustrates how tests are performed in a hardware-in-the-loop setup specifically developed for testing platooning functions. The availability of a test method that is capable of dealing with V2X communication is an important step towards the implementation of type approval methods for Cooperative, Connected and Automated Mobility (CCAM) systems.

Acceptability of intelligent speed adaptation technology in developing Countries: The case of Ethiopian minibus taxi drivers

Insight in the acceptability of Intelligent Speed Adaptation (ISA) is an important requirement for the supported implementation of speed limiter technology. The aim of this study was to investigate the behavioural intention to accept ISA technology in a sample of 218 male minibus taxi drivers in Addis Ababa, Ethiopia. The hypothesized framework proposed attitude, effort expectancy, performance expectancy and social influence as determinants of the behavioural intention to accept ISA technology. It was found that positive evaluation towards using ISA technology and the degree of convenience associated with the use of this technology are likely to represent target goals for interventions aimed to promote the use of such technology among minibus taxi drivers. These results have also practical implications for the Ethiopian Transport Authority which is taking the initiative to large scale deployment of in-vehicle speed control technology in Ethiopia.

Revisiting Speed Management Strategies in Malaysia


 
 
 
 Speed, because of its centrality to mobility, deserves effective management approaches. This review examined the present approaches in Malaysia: speed zoning, enforcement, education, and physical engineering; before putting forward evidence of their limited effectivity. The discussion continued with a proposal for preventive approaches in managing speed. Instead of reactive measures, i.e. curbing the speeding, the proposal clutches on the elimination of room for speeding by advocating for vehicular speed management system – e.g. the intelligent speed adaptation technology, or top speed limiting by design. The potential regulatory mechanism, implementation challenges, and subsequent courses of action to realize the proposal concludes the article.
 
 
 

Development of a Novel Intelligent Speed Adaptation System Based on Available Sight Distance

Most existing roads were designed without considering the improved performance of modern vehicles and the new onboard technologies available for assisted driving. In addition, vehicles frequently travel at speeds that exceed the maximum considered in road design. For these reasons, the need for speed- and safety-related countermeasures (e.g., field control, mobile or fixed speed cameras, traffic calming measures) is evident. However, these countermeasures are only partially effective and the proportion of crashes that are speed-related remains significant. This investigation is aimed at the development of a new intelligent speed adaptation (ISA) system based on the available sight distance (ASD). In conditions of poor visibility, the system can (i) inform drivers when they are traveling at inappropriate speeds, or (ii) generate warning sounds to the same effect, or (iii) intervene directly and compel the vehicle to adopt the speed which is most appropriate to the particular ASD. As reported in this methodological paper, the functionality of the new ISA system was tested at the driving simulator of the Politecnico di Torino (Italy) and the resulting estimated ASD value was validated and tested successfully. Future experimental investigations will be devoted to assessing the effectiveness of the system on driver speed behavior and decision making.

ROAD SAFETY: A MAJOR PUBLIC HEALTH ISSUE

ROAD SAFETY: A MAJOR PUBLIC HEALTH ISSUE

Speeding and impaired driving in fatal crashes—Results from in-depth investigations

Objective The aim of the study was to identify typical patterns of risk factors among speeding and impaired drivers involved in fatal crashes and to suggest countermeasures. Method Results from in-depth investigations of 1,501 fatal passenger car crashes that occurred in Norway from 2005 to 2015 and involved 1,949 passenger cars are summarized. Relationships between speeding (excessive speed, EXC, or inappropriate speed, INAP); driving under the influence (DUI) of alcohol (ALC), drugs (DRUG), or both (MIX); and other driver-, crash-, vehicle-, and road-related variables were investigated. Results Speeding and DUI drivers have in common that they are more often male, unbelted, unlicensed, driving old cars, and involved in single-vehicle crashes under low-volume conditions (nighttime, weekend, low-volume roads). They are also less often fatigued, ill, or suicidal. EXC and ALC drivers are on average younger and they generally show more high-risk behavior than INAP, DRUG, and MIX drivers. In crashes with pedestrians/cyclists and crashes on slippery roads or in winter, INAP drivers are overrepresented and EXC and DUI drivers are underrepresented. Among DRUG and MIX drivers there may be differences according to the type of substance. Conclusions Those drivers who show most types of high-risk behaviors, especially EXC and ALC drivers, are less likely to respond to measures relying on voluntary behavior changes, such as recommended speed, speed warnings, or classical enforcement, although such measures may be effective for INAP and some DRUG drivers. To change the behavior of EXC and ALC drivers, more restrictive measures are called for, such as non-overridable intelligent speed adaptation, vehicle impoundment, or Alcolock.

Preface

2019 International Conference on Intelligent Transportation and Vehicle Engineering (ICITVE 2019), hosted by School of Automotive and Transportation Engineering, Heilongjiang Institute of Technology, was successfully held in Chongqing, China during December 6-8, 2019. ICITVE 2019 aims to provide a platform for experts, scholars, and practitioners in the field of Intelligent Transportation and Vehicle Engineering to share the latest research results, discuss existing problems and challenges, explore cutting-edge technologies, and enhance their cooperation.We are honored to have Prof. Baixue Fu, Dean of School of Automotive and Transportation Engineering, Heilongjiang Institute of Technology, China, and Prof. Traian Mazilu from University Politehnica of Bucharest, Romania to jointly chair this conference. And we invited over 30 excellent scholars form east and west to constitute the Academic Committee of ICITVE 2019. During the conference, five remarkable experts gave excellent keynote speeches related to the field of Intelligent Transportation and Vehicle Engineering on site. Prof. Devinder Yadav from University of Nottingham Ningbo, China discussed emission control at airports and advances in alternate propulsion in civil aviation industry. Assoc.Prof. Seyed Mohammadreza Ghadiri from Malaysia University of Science & Technology, shared his research on Speed Limit Obedience Factor (SLOF) A New Countermeasure to Improve the Efficiency of The Advisory Intelligent Speed Adaptation. Prof. Said Easa from Ryerson University Toronto, Canada held a speech on Smart Mobility: Challenges and Solutions. Prof. Fangwu Ma from Jilin University, China discussed the Outlook of Ecosystem of New Mobility. And Senior Engineer Chengyong Niu, Project Supervisor from Chongqing Vehicle Test & Research Institute Co., Ltd, China had a speech on Test and application challenges for autonomous vehicle at level 3 and below. Thanks to these wonderful speeches given by the distinguished experts, the conference has formed a lively and heated academic vibe on site.We are happy to present the proceedings of ICITVE 2019 featured the new advances and research results in the fields of transportation and vehicle engineering research. All papers have been through rigid review process to comply with the requirements of international publication.We would to like to express our sincere gratitude to all the participants attending the conference, who made this conference a great success. We want to thank our honorific chairmen for hosting this conference, the distinguished keynote speakers for giving insightful speeches, and all authors for submitting their study paper. We hope this conference and the proceedings of it could serve as a good reference for those who work in the field of Intelligent Transportation and Vehicle Engineering. We aim to hold this conference every year to make it an convenient platform for people to share views and experiences in intelligent transportation and vehicle engineering and related areas. We would like to see you next year. Thank you.Committee of ICITVE 2019List of Committee members are available in this pdf.

Hierarchical Bayesian modeling to evaluate the impacts of intelligent speed adaptation considering individuals’ usual speeding tendencies: A correlated random parameters approach

Although there have been a large number of short- and long-term field trials and driving simulator-based studies on intelligent speed adaptation (ISA), there are a limited number of methods for evaluating the impact of ISA. In particular, appropriately evaluating the impacts of ISA through field trials requires the consideration of a number of factors related to detailed road characteristics. In addition, because of the “target speed” features of ISA itself, it is necessary to consider the usual speeding tendencies of individual drivers to avoid underestimating the impact of the ISA. In this study, a hierarchical Bayesian model with correlated random effects was developed to examine the impact of ISA on driver speed based on the driver’s typical speeding tendency and road characteristic conditions as confounding factors. The model was applied to clarify and compare the impacts of informative and incentive ISAs on community streets with a 30 km/h speed limit (“Zone 30”) based on data collected in a field trial. The modeled effects of many road characteristics were found to match intuitive expectations, suggesting that they were well-controlled in our assessment of the ISAs’ impacts. It was also confirmed that the incentive ISA had a large speed reduction effect on the behavior of drivers who tended to speed, while the informative ISA did not. In particular, although the impact of the incentive ISA on all drivers was only 2 km/h greater on average than that of the informative ISA, the impact of the incentive ISA on drivers who tended to speed was 7 km/h greater than that of the informative ISA when correlated random effects were considered.

Examination of the viability of co-operative road-vehicle systems using qualitative data analysis

Co-operative road-vehicle systems, such as dynamic navigation and in a later stadium traffic-responsive adaptive cruise control, are expected to contribute to traffic safety and efficiency. However, it is not yet clear which concepts of co-operative systems would be viable from an implementation point of view. Therefore, the objective of the research presented in this paper was to gain knowledge into the expectations about co-operative road- vehicle systems and the driving forces and barriers of stakeholders. To obtain this information, semi-structured interviews were held with six experts and seventeen stakeholders. The qualitative interview data was structured in a database that was used to perform the analysis. Of all the concepts of co-operative road-vehicle systems mentioned by the interview participants, five were recognised as potentially viable: Navigation systems, Intelligent Speed Adaptation (ISA), Traffic responsive Adaptive Cruise Control, Intersection support and Information systems. A deployment path for these systems was constructed based on the two main routes for deployment recognised that focused on Telematics and Advanced Driver Assistance Systems respectively. Two viable concepts were identified at the point where positive expectations about cooperative road-vehicle systems and the driving forces of stakeholders coincided. Obligatory half-open ISA relieves barriers such as market penetration and profitability and is expected to have positive effects on efficiency and safety. A multifunctional information platform would increase efficiency for commercial transportation.

Examining Speed Compliance Effects of Advisory Intelligent Speed Adaptation to Elder Drivers in Maximum Speed Decline Intersections

Examining Speed Compliance Effects of Advisory Intelligent Speed Adaptation to Elder Drivers in Maximum Speed Decline Intersections

Driver acceptance of mandatory intelligent speed adaptation

This paper examines driver acceptance of mandatory ISA. This intervening system that prevents the driver from exceeding local limits runs the risk of insufficient support of stakeholders and drivers because behaviour is restrained. At the same time, the expected impact on efficiency and traffic safety might be greater than the effects of permissive systems. Measuring driver acceptance was a major objective of a Dutch field test involving 120 test drivers who used the mandatory ISA system during six weeks. The research design included three different reference groups with varying degree of ISA exposure. The major research question addressed in the paper is the acceptance level of mandatory ISA among drivers who experience the system. The related question whether acquaintance with automatically enforced speed adaptation goes with either a lower or a higher level of acceptance is discussed. The acceptance data show considerable similarities with the Hawthorne effect. It is concluded that the level of acceptance of mandatory ISA is fairly high - higher than expected - and that experience results in a higher acceptance level.