Digital twin—The dream and the reality

Digital twins (DTs) are under active research and development in the research community, industry, and in the digital engineering solution business. The roots of the concept of DT are almost 2 decades old, but the fast progress in enabling technologies, especially in data analytics, artificial intelligence, and the Internet of Things, has accelerated the evolution of DT during the last 5 years. The growing interest, increasing development activities, and increasing business opportunities of the concept are also feeding the hype in the media. Consequently, this has led to the scattering and even misuse of the concept and its definition. In this article, we discuss different applications of DTs and what kinds of solutions there are for DTs. We analyze some most cited definitions of DT in the scientific literature and discuss the interpretation of the definitions through a hypothetical case example. Furthermore, we discuss different life cycle aspects of DTs and potential risks that may arise. To further concretize the concept of DT, we introduce ten reported case examples of implemented DTs in the scientific literature and analyze their features. Finally, we discuss the future development directions of DTs and the aspects that will affect the development trends

Influence of Increasing Electrification of Passenger Vehicle Fleet on Carbon Dioxide Emissions in Finland

Different estimations have been presented for the amount of electric vehicles in the future. These estimations rarely take into account any realistic dynamics of the vehicle fleet. The objective of this paper is to analyze recently presented future scenarios about the passenger vehicle fleet estimations and create a foundation for the development of a fleet estimation model for passenger cars dedicated to the Finnish vehicle market conditions. The specific conditions of the Finnish light-duty vehicle fleet are taken into account as boundary conditions for the model development. The fleet model can be used for the estimation of emissions-optimal future vehicle fleets and the evaluation of the carbon dioxide emissions of transportation. The emission analysis was done for four different scenarios of the passenger vehicle fleet development in Finland. The results show that the high average age of the fleet and high number of older gasoline vehicles will slow down the reduction of carbon dioxide emissions during the next five to ten years even with a high adoption rate of electric vehicles. It can be concluded that lowering the average age, increasing biofuel mixing ratios, and increasing the amount of rechargeable electric vehicles are the most effective measures to reduce carbon dioxide emissions of the Finnish passenger vehicle fleet in the future.Peer reviewe

Predictive Braking With Brake Light Detection-Field Test

Driver assistance systems, such as adaptive cruise control, are an increasing commodity in modern vehicles. Our earlier experience of radar-based adaptive cruise control has indicated repeatable abrupt behavior when approaching a stopped vehicle at high speed, which is typical for extra-urban roads. Abrupt behavior in assisted driving not only decreases the passenger trust but also reduces the comfort levels of such systems. We present a design and proof-of-concept of a machine vision-enhanced adaptive cruise controller. A machine vision-based brake light detection system was implemented and tested in order to smoothen the transition from coasting to braking and ensure speed reduction early enough. The machine vision system detects the brake lights in front, then transmits a command to the cruise controller to reduce speed. The current paper reports the speed control system design and experiments carried out to validate the system. The experiments showed the system works as designed by reducing abrupt behavior. Measurements show that brake light-assisted cruise control was able to start deceleration about three seconds earlier than a cruise controller without brake light detection. Measurements also showed increased ride comfort with the maximum deceleration and minimum jerk levels improving from 5% to 31%.Peer reviewe

Comparison of Semi-autonomous Mobile Robot Control Strategies in Presence of Large Delay Fluctuation

We propose semi-autonomous control strategies to assist in the teleoperation of mobile robots under unstable communication conditions. A short-term autonomous control system is the assistance in the semi-autonomous control strategies, when the teleoperation is compromised. The short-term autonomous control comprises of lateral and longitudinal functions. The lateral control is based on an artificial potential field method where obstacles are repulsive, and a route is attractive. LiDAR-based artificial potential field methods are well studied. We present a novel artificial potential field method based on color and depth images. Benefit of a camera system compared to a LiDAR is that a camera detects color, is cheaper, and does not have moving parts. Moreover, utilization of active sensors is not desired in the particle accelerator environment. A set of experiments with a robot prototype are carried out to validate this system. The experiments are carried out in an environment which mimics the accelerator tunnel environment. The difficulty of the teleoperation is altered with obstacles. Fully manual and autonomous control are compared with the proposed semi-autonomous control strategies. The results show that the teleoperation is improved with autonomous, delay-dependent, and control-dependent assist compared to the fully manual control. Based on the operation time, control-dependent assist performed the best, reducing the time by 12% on the tunnel section with most obstacles. The presented system can be easily applied to common industrial robots operating e.g. in warehouses or factories due to hardware simplicity and light computational demand.Peer reviewe

Brake Light Detection Algorithm for Predictive Braking

There has recently been a rapid increase in the number of partially automated systems in passenger vehicles. This has necessitated a greater focus on the effect the systems have on the comfort and trust of passengers. One significant issue is the delayed detection of stationary or harshly braking vehicles. This paper proposes a novel brake light detection algorithm in order to improve ride comfort. The system uses a camera and YOLOv3 object detector to detect the bounding boxes of the vehicles ahead of the ego vehicle. The bounding boxes are preprocessed with L*a*b colorspace thresholding. Thereafter, the bounding boxes are resized to a 30 × 30 pixel resolution and fed into a random forest algorithm. The novel detection system was evaluated using a dataset collected in the Helsinki metropolitan area in varying conditions. Carried out experiments revealed that the new algorithm reaches a high accuracy of 81.8%. For comparison, using the random forest algorithm alone produced an accuracy of 73.4%, thus proving the value of the preprocessing stage. Furthermore, a range test was conducted. It was found that with a suitable camera, the algorithm can reliably detect lit brake lights even up to a distance of 150 m

Brake Light Detection Algorithm for Predictive Braking

There has recently been a rapid increase in the number of partially automated systems in passenger vehicles. This has necessitated a greater focus on the effect the systems have on the comfort and trust of passengers. One significant issue is the delayed detection of stationary or harshly braking vehicles. This paper proposes a novel brake light detection algorithm in order to improve ride comfort. The system uses a camera and YOLOv3 object detector to detect the bounding boxes of the vehicles ahead of the ego vehicle. The bounding boxes are preprocessed with L*a*b colorspace thresholding. Thereafter, the bounding boxes are resized to a 30 × 30 pixel resolution and fed into a random forest algorithm. The novel detection system was evaluated using a dataset collected in the Helsinki metropolitan area in varying conditions. Carried out experiments revealed that the new algorithm reaches a high accuracy of 81.8%. For comparison, using the random forest algorithm alone produced an accuracy of 73.4%, thus proving the value of the preprocessing stage. Furthermore, a range test was conducted. It was found that with a suitable camera, the algorithm can reliably detect lit brake lights even up to a distance of 150 m

Reducing the energy consumption of electric buses with design choices and predictive driving

Peer reviewe

Energy Uncertainty Analysis of Electric Buses

Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.Peer reviewe

Cost-Benefit Analysis of Electric Bus Fleet with Various Operation Intervals

Electric buses are particularly suitable for city and suburban routes due to zero local exhaust and noise emissions. The operation schedule interval defines the charging power, bus fleet size and total cost of ownership of a bus. We propose a novel cost-benefit method for the scheduling of an electric city bus fleet on a single route. Three different charging infrastructure scenarios were considered. In the first scenario, only one charging station was used. The second scenario considered two charging stations that were located at the same terminus. In the third scenario, two charging stations were located at opposite terminuses. The costs and utilization rates of the buses were analyzed with operation intervals up to 40 minutes. The first scenario with a single charging station had the lowest costs for the entire bus fleet system when the utilization rate was considered. Furthermore, the results show that certain schedule intervals are more cost-beneficial in terms of vehicle specific life-cycle costs than others. In the future, the proposed method is expanded to aid the design of bus network scheduling under energy demand uncertainty.Peer reviewe