Geospatial truck parking locations data for Europe.

This data article introduces a comprehensive dataset of real-world truck parking locations across Europe. The dataset comprises N = 19,713 designated parking sites classified according to public accessibility and suitability for heavy-duty trucks (HDTs). More specifically, core information comprises the truck stop category, latitude and longitude information, area size, and country assignment. Furthermore, additional information such as truck traffic flow volumes, proximity to the highway network, and land use information provide supplemental data on ambient conditions and thus enhance the contextual relevance of those locations. The dataset was systematically generated using OpenStreetMap (OSM) data, focusing on parking areas, rest areas, and fueling stations as predominant public truck parking sites. These locations were evaluated and filtered for truck accessibility and suitability and then complemented and validated using commercial truck routing / geocoding software. Further refinement was achieved by Mean-Shift clustering. The further integration of supplementary datasets increased the information level, and all clustered locations were labeled into four archetypal categories. Finally, filtering retained only confidently classified publicly accessible and truck-certified parking and service facilities. This dataset assists in finding real-world stop options for HDTs during national or international operations and identifying suitable and most attractive sites for deploying alternative charging or refueling infrastructures along the European transport network. Accordingly, it can serve as a valuable resource for research in traffic science, future energy systems, and alternative truck powertrains. Its added value extends to diverse stakeholders like Charge Point Operators (CPOs), truck manufacturers, logistics companies, and public authorities.

Beyond the surface: Exploring the temporally stable factors influencing injury severities in large-truck crashes using mixed logit models.

An analysis of crash data spanning four years (January 1, 2015, to December 31, 2018) from the State of Washington is conducted to investigate factors influencing injury severity outcomes in large truck-involved crashes. The study utilizes a mixed logit model that accounts for unobserved heterogeneity to capture the variation influenced by other variables. Transferability and temporal stability across the years are assessed using the likelihood ratio test. A wide range of attributes, including driver characteristics, vehicle features, crash-related attributes, roadway conditions, environmental factors, and temporal elements, are considered. Despite a significant temporal instability warranted by the likelihood ratio test across the years, twenty-one parameters consistently exhibit stable effects on injury severity over the years of which thirteen are new. The identified stable parameters included over speeding, following too closely, falling asleep, missing/ faulty airbags, head-on collisions, crashes involving two or more than three vehicles, rear-end collisions, lane width, low-light conditions, sag curves, New Jersey barriers, snowy weather, and morning hours. The temporally stable factors affecting injury severities in large truck crashes are crucial in developing the needed to address these crashes. The findings of this study offer valuable insights for researchers, stakeholders in the trucking industry, and policymakers, empowering them to develop targeted policies that not only improve traffic safety but also alleviate associated economic losses.

A deep learning based encoder-decoder model for speed planning of autonomous electric truck platoons.

Electric truck platooning offers a promising solution to extend the range of electric vehicles during long-haul operations. However, optimizing the platoon speed to ensure efficient energy utilization remains a critical challenge. The existing research on implementing data-driven solutions for truck platooning remains limited and implementing first principles solution is still a challenge. However, recognizing the resemblance of truck platoon data to a time series serves as a compelling motivation to explore suitable analytical techniques to address the problem. This paper presents a novel deep learning approach using a sequence-to-sequence encoder-decoder model to obtain the speed profile to be followed by an autonomous electric truck platoon considering various constraints such as the available state of charge (SOC) in the batteries along with other vehicles and road conditions while ensuring that the platoon is string stable. To ensure that the framework is suitable for long-haul highway operation, the model has been trained using various known highway drive cycles. Encoder-decoder models were trained and hyperparameter tuning was performed for the same. Finally, the most suitable model has been chosen for the application. For testing the entire framework, drive cycle/speed prediction corresponding to different desired SOC profiles has been presented. A case study showing the relevance of the proposed framework in predicting the drive cycle on various routes and its impact on taking critical policy decisions during the planning of electric truck platoons has also been presented. This study would help to efficiently plan the feasible routes for electric trucks considering multiple constraints such as battery capacity, expected discharge rate, charging infrastructure availability, route length/travel time, and other on-road operating conditions while also maintaining stability.

Bi-objective optimization for last-mile routing of garbage trucks: Aesthetics and Public exposure.

In the area of Solid Waste Management, transportation of the collected waste is a critical aspect considering the substantial time spent by garbage trucks on public roads. Studies have reported that transporting garbage has challenges related to public exposure and aesthetics. This study presents a generalised bi-objective formulation for the optimal routing of garbage trucks from transfer stations to recycling sites/landfills considering the trade-off between public exposure and aesthetic loss and constraining the operating cost. The formulation uses the novel link capacity function to account for the delay at traffic signals and the mix of trucks and cars on link performance. The proposed formulation is solved using the weighted sum and ε-constraint methods and applied to a realistic case study of the City of Chicago, USA. The Pareto Front obtained for the bi-objective formulation offers diverse trade-off solutions catering to distinct performance metrics. The results highlight the disparity across the solutions; the solution (P0.95 on Pareto Front) for minimum operating cost (or travel time or distance travelled) is very different from the solution (P0.4 on Pareto Front) for aesthetic cost and public exposure. The parametric study indicated that a modest operating budget may suffice for achieving aesthetic benefits, but minimising public exposure requires a higher operating budget. Finally, the proposed framework is adaptable to address various challenges pertaining to waste transportation, thereby serving as a valuable tool for evaluating policies and practices geared towards sustainability objectives.

Real-world greenhouse gas emission characteristics from in-use light-duty diesel trucks in China.

The transportation sector is a significant contributor to greenhouse gas (GHG) emissions in China. However, real-world GHG emissions from in-use light-duty diesel trucks (LDDTs) are largely uncertain due to data paucity. In this study, we have conducted real driving emission (RDE) tests of real-world CO2, N2O, and CH4 emissions from 12 in-use LDDTs in China. Results reveal that China's CH4 emission rates from LDDTs are overestimated by 57.71 ± 39.15 % if using the previous ratio method of CO2:CH4. Notably, under real-world driving conditions, such as speeds exceeding 60 km/h, maximum exhaust gas temperatures are reached, potentially impacting urea decomposition catalyst temperatures and subsequently influencing N2O production, which is highly sensitive to system temperature. Moreover, uphill roads can increase CO2 emissions by 51.93 % compared to downhill roads. Despite the tightening of vehicle emission standards, CO2 and N2O emissions from the LDDTs have not decreased linearly. However, LDDTs meeting the China VI standard exhibit the lowest average CO2, N2O and CH4 emission factors (EFs) of 335.26 ± 21.72 g/km, 2.7 ± 0.69 mg/km and 3.50 ± 0.70 mg/km, respectively. At last, the uncertainties in the GHG EFs for the tested LDDTs through RDE tests were (-39 %, 82 %) in our study, while a significantly higher uncertainty (-85 %, 182 %) for GHG EFs of LDDTs were found in our study and other reported literature in China, largely due to the application of different non-native vehicle emission factor models and testing methods, as well as different vehicles of control emission standards. Our study highlights more urgent needs for direct RDE tests and the importance of considering real driving conditions, such as road grades, in special geographical regions when undertaking carbon accounting work in the transportation sector.

On-road evaluation and regulatory recommendations for NOx and particle number emissions of China VI heavy-duty diesel trucks: A case study in Shenzhen.

This research analyzed the real-world NOx and particle number (PN) emissions of 21 China VI heavy-duty diesel trucks (HDDTs). On-road emission conformity was first evaluated with portable emission measurement system (PEMS). Only 76.19 %, 71.43 % and 61.90 % of the vehicles passed the NOx test, PN test and both tests, respectively. The impacts of vehicle features including exhaust gas recirculation (EGR) equipment, mileage and tractive tonnage were then assessed. Results demonstrated that EGR helped reducing NOx emission factors (EFs) while increased PN EFs. Larger mileages and tractive tonnages corresponded to higher NOx and PN EFs, respectively. In-depth analyses regarding the influences of operating conditions on emissions were conducted with both numerical comparisons and statistical tests. Results proved that HDDTs generated higher NOx EFs under low speeds or large vehicle specific powers (VSPs), and higher PN EFs under high speeds or small VSPs in general. In addition, unqualified vehicles generated significantly higher NOx EFs than qualified vehicles on freeways or under speed≥40 km/h, while significant higher PN EFs were generated on suburban roads, freeways or under operating modes with positive VSPs by unqualified vehicles. The reliability and accuracy of on-board diagnostic (OBD) NOx data were finally investigated. Results revealed that 43 % of the test vehicles did not report reliable OBD data. Correlation analyses between OBD NOx and PEMS measurements further demonstrated that the consistency of instantaneous concentrations were generally low. However, sliding window averaged concentrations show better correlations, e.g., the Pearson correlation coefficients on 20s-window averaged concentrations exceeded 0.85 for most vehicles. The research results provide valuable insights into emission regulation, e.g., focusing more on medium- to high-speed operations to identify unqualified vehicles, setting higher standards to improve the quality of OBD data, and adopting window averaged OBD NOx concentrations in evaluating vehicle emission performance.

Regulatory Insights for On-Board Monitoring of Vehicular NOx Emission Compliance.

Nitrogen oxide (NOx) emissions from heavy-duty diesel vehicles (HDDVs) have adverse effects on human health and the environment. On-board monitoring (OBM), which can continuously collect vehicle performance and NOx emissions throughout the operation lifespan, is recognized as the core technology for future vehicle in-use compliance, but its large-scale application has not been reported. Here, we utilized OBM data from 22,520 HDDVs in China to evaluate their real-world NOx emissions. Our findings showed that China VI HDDVs had a 73% NOx emission reduction compared with China V vehicles, but a considerable proportion still faced a significant risk of higher NOx emissions than the corresponding limits. The unsatisfactory efficiency of the emission treatment system under disadvantageous driving conditions (e.g., low speed or ambient temperature) resulted in the incompliance of NOx emissions, especially for utility vehicles (sanitation/garbage trucks). Furthermore, the observed intertrip and seasonal variability of NOx emissions demonstrated the need for a long-term continuous monitoring protocol instead of instantaneous evaluation for the OBM. With both functions of emission monitoring and malfunction diagnostics, OBM has the potential to accurately verify the in-use compliance status of large-scale HDDVs and discern the responsibility of high-emitting activities from manufacturers, vehicle operators, and driving conditions.

Lateral safety evaluation of autonomous trucks in overtaking maneuvers and adverse weather: Exploratory comparison of different lateral control modes.

As the detrimental impact of the commonly recommended centered driving mode for autonomous trucks on road longevity is gaining attention, more lateral control modes are being proposed to enhance road sustainability. However, there is currently a lack of research on the lateral safety analysis of autonomous trucks with different lateral control modes, especially in complex driving scenarios (such as overtaking) and adverse weather conditions. Therefore, this study developed a safety assessment framework to comparatively analyze the risk probability differences in lateral accidents during overtaking maneuvers by autonomous trucks with different lateral control modes under adverse weather conditions. Based on aerodynamics and vehicle dynamics simulations to capture the multifactorial influences on truck lateral deviation, the results are used for model validation and training. In the reliability approach, Support Vector Machine Regression (SVR) is introduced to establish the SVR response surface model with optimal predictive performance, and combined with Monte Carlo simulations for safety assessment, quantifying safety indices. The results indicate that trucks being overtaken during overtaking maneuvers are more prone to lateral accidents under crosswind influences. The overall impact of lateral control modes on the lateral safety trends is minor. Compared to other lateral control modes, following the centered zero-drift mode is generally safer. However, in conditions of low wind speeds (below 20 km/h) or on highly slippery road surfaces (road friction coefficient below 0.1), autonomous trucks following a uniform distribution mode can better maintain a low-risk level. This study provides crucial insights for future considerations integrating road longevity and truck safety in a collaborative manner, and the proposed methodology has broad applications.

Corridor-Level Impacts of Battery-Electric Heavy-Duty Trucks and the Effects of Policy in the United States.

Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.

Towards sustainable transport: quantifying and mitigating pollutant emissions from heavy-duty diesel trucks in Northeast China.

Heavy-duty diesel trucks (HDDTs) have caused serious environmental pollution in China. Accurate estimation of their pollutant emission characteristics is essential to reduce emissions and associated environmental and public health impacts. To achieve sustainable development for transport emissions in Northeast China, we developed localized emission factors and a high-resolution emission inventory of HDDTs, based on on-board test, Guidebook and international vehicle emission (IVE) model. The results show that the total emissions of CO, NO, NO2, and PM from HDDTs in Northeast China in 2020 were 172.2 kt, 531.5 kt, 11.2 kt, and 921.4 t, respectively. In terms of spatial distribution, emissions decreased from the city center to the city fringe. Temporally, the NOx emission variation curves of different types of roads presented a "single-peak" emission characteristic, which was different from the peak of traffic flow. Three emission reduction scenarios are further developed in the paper. Scenario analysis shows that elimination of HDDTs that follow the old China III emission standard and installing tailpipe treatment devices are the most effective pollutant reduction measure. The reduction percentages for CO, NO, NO2, and PM ranged from 62.9 to 83.89%. The results of our study could inform policymakers to devise feasible strategies to reduce vehicle pollution in Northeast China.

The effects of local interventions on global technological change through spillovers: A modeling framework and application to the road-freight sector.

To address global sustainability challenges, (public) policy interventions are needed to induce or accelerate technological change. While most policy interventions occur on the local level, their innovation effects can spill over to other jurisdictions, potentially having global impact. These spillovers can increase or reduce the incentive for interventions. Lacking to date are computational models that capture these spillover dynamics. Here, we devise a conceptual and methodological approach to quantify ex ante the effects of local demand-side interventions on global competition between incumbent and novel technologies. We introduce two factors that moderate global spillovers-relative size of selection environments and relative innovation potential of competing technologies. Our approach incorporates both factors in a techno-economic discrete choice model that evaluates technology competition over time through endogenized technological learning. We apply this modeling framework to the case of road freight. Different demand-pull interventions and shocks are modeled to assess spillover effects. In the case of road freight, electric vehicles experience growth in most application segments but can still be accelerated substantially through public policy intervention-spillovers occur if strong public interventions are introduced in large regions or in multiple combined regions under club policy interventions. These findings are discussed in the context of club policy interventions and a modeled geopolitical shock in China. A full sensitivity analysis of model input parameters and intervention or shock dynamics reveals high model robustness. Finally, we discuss the implications of the road-freight case study as it might inform the progress of other niche technologies in transitioning sectors.

Emission Measurements on a Large Sample of Heavy-Duty Diesel Trucks in China by Using Mobile Plume Chasing.

Real-world heavy-duty diesel trucks (HDTs) were found to emit far more excess nitrogen oxides (NOX) and black carbon (BC) pollutants than regulation limits. It is essential to systematically evaluate on-road NOX and BC emission levels for mitigating HDT emissions. This study launched 2109 plume chasing campaigns for NOX and BC emissions of HDTs across several regions in China from 2017 to 2020. It was found that NOX emissions had limited reductions from China III to China V, while BC emissions of HDTs exhibited high reductions with stricter emission standard implementation. This paper showed that previous studies underestimated 18% of NOX emissions in China in 2019 and nearly half of the real-world NOX emissions from HDTs (determined by updating the emission trends of HDTs) exceeded the regulation limits. Furthermore, the ambient temperature was identified as a primary driver of NOX emissions for HDTs, and the low-temperature penalty has caused a 9-29% increase in NOX emissions in winter in major regions of China. These results would provide important data support for the precise control of the NOX and BC emissions from HDTs.

Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks.

With the advancement of vehicle electrification and intelligence, distributed drive electric trucks have emerged as the preferred choice for heavy-duty electric trucks. However, the control of yaw stability remains a significant issue. To tackle this concern, this study introduces a layered control strategy for yaw moment. Specifically, the upper layer utilizes a yaw moment controller based on linear quadratic regulator (LQR) to compute the additional yaw moment required. Additionally, in order to enhance the performance of the yaw moment controller, the weight matrix in LQR is optimized using a hybrid Genetic Algorithm and Particle Swarm Optimization algorithm (GA-PSO). The lower layer consists of a torque distribution layer, which establishes an objective function for minimizing tire utilization rate. Quadratic Programming algorithm is then employed to compute the optimal torque distribution value, thereby improving the vehicle's stability. Subsequently, the stability control effects of the vehicle are simulated and compared on the Matlab/Simulink Trucksim joint simulation platform using four control strategies: the proposed control strategy, SMC, LQR, and without yaw moment control. These simulations are conducted under two working conditions: serpentine and double lane change. The results demonstrate that the proposed approach reduces the average yaw rate by 14.4%, 19.6%, and 42.15% while optimizing the average sideslip angle by 25.9%, 24.8%, and 52.3% in comparison to the other three control strategies. Consequently, the proposed control strategy significantly enhances the driving stability of the vehicle. Furthermore, the optimized allocation method reduces the average tire utilization rate by 42.6% in contrast to the average allocation method, thereby improving the stability control margin of the vehicle. These findings successfully validate the efficiency of the yaw stability control strategy presented in this article.

Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing.

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the "business as usual" scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

Sustainable selection of waste collection trucks considering feasible future scenarios by applying the stratified best and worst method.

Municipal solid waste (MSW) management is vital in achieving sustainable development goals. It is a complex activity embracing collection, transport, recycling, and disposal; and whose management depends on proper strategic decision-making. The use of decision support methods such as multi-criteria decision-making (MCDM) is widespread in MSW management. However, their application mainly focuses on selecting plant locations and the best technologies for waste treatment. Despite the critical role played by transport in promoting sustainability, MCDM has seldom been applied for the selection of sustainable transport alternatives in the field of MSW management. There are a few MCDM studies about choosing waste collection vehicles, but none that include the most recent green vehicles among the options or consider feasible future scenarios. In this article, different engine technologies for collection trucks (diesel, compressed natural gas (CNG), hybrid CNG-electric, electric, and hydrogen) are evaluated under sustainability criteria in a Spanish city by applying the stratified best and worst method (SBWM). This method enables considering the uncertainty associated with future events to establish various feasible scenarios. The results show that the best-valued options are electric and diesel trucks, in that order, followed by CNG and hybrid CNG-electric, and with hydrogen-powered trucks coming last. The SBWM has proven helpful in defining a comprehensive framework for selecting the most suitable engine technology to support long-term MSW collection. Considering sustainability among the criteria and feasible future scenarios in waste management collection decision-making provides more comprehensive and conclusive results that help managers and policymakers make better informed and more reliable decisions.

Development of a Particle Filter-Based Path Tracking Algorithm of Autonomous Trucks with a Single Steering and Driving Module Using a Monocular Camera.

Recently, in various fields, research into the path tracking of autonomous vehicles and automated guided vehicles has been conducted to improve worker safety, convenience, and work efficiency. For path tracking of various systems applied to autonomous driving technology, it is necessary to recognize the surrounding environment, determine technology accordingly, and develop control methods. Various sensors and artificial-intelligence-based perception methods have limitations in that they must learn a large amount of data. Therefore, a particle-filter-based path tracking algorithm using a monocular camera was used for the recognition of target RGB. The path tracking errors were calculated and a linear-quadratic-regulator-based desired steering angle were derived. The autonomous trucks were steered and driven using a pulse-width-modulation-based steering and driving motor. Based on an autonomous truck with a single steering and driving module, it was verified that the path tracking could be used in three evaluation scenarios. To compare the LQR-based path tracking control performance proposed in this paper, an elliptical path tracking scenario using a conventional sliding mode control with robust control performance was performed. The results show that the RMS of the lateral preview error of the SMC was approximately 18% larger than that of the LQR-based method.

Temporal-spatial dynamic characteristics of vehicle emissions on intercity roads in Guangdong Province based on vehicle identity detection data.

Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple cities. Considering the variability of emissions caused by vehicles, roads, and traffic, the 24-hour change characteristics of air pollutants (CO, HC, NOX, PM2.5) on the intercity road network of Guangdong Province by vehicle categories and road links were revealed based on vehicle identity detection data in real-life traffic for each hour in July 2018. The results showed that the spatial diversity of emissions caused by the unbalanced economy was obvious. The vehicle emissions in the Pearl River Delta region (PRD) with a higher economic level were approximately 1-2 times those in the non-Pearl River Delta region (non-PRD). Provincial roads with high loads became potential sources of high emissions. Therefore, emission control policies must emphasize the PRD and key roads by travel guidance to achieve greater reduction. Gasoline passenger cars with a large proportion of traffic dominated morning and evening peaks in the 24-hour period and were the dominant contributors to CO and HC emissions, contributing more than 50% in the daytime (7:00-23:00) and higher than 26% at night (0:00-6:00). Diesel trucks made up 10% of traffic, but were the dominant player at night, contributed 50%-90% to NOX and PM2.5 emissions, with a marked 24-hour change rule of more than 80% at night (23:00-5:00) and less than 60% during daytime. Therefore, targeted control measures by time-section should be set up on collaborative control. These findings provide time-varying decision support for variable vehicle emission control on a large scale.

Surface Hygiene Evaluation Method in Food Trucks as an Important Factor in the Assessment of Microbiological Risks in Mobile Gastronomy.

Street food outlets are characterised by poor microbiological quality of the food and poor hygiene practices that pose a risk to consumer health. The aim of the study was to evaluate the hygiene of surfaces in food trucks (FT) using the reference method together with alternatives such as PetrifilmTM and the bioluminescence method. TVC, S. aureus, Enterobacteriaceae, E. coli, L. monocytogenes, and Salmonella spp. were assessed. The material for the study consisted of swabs and prints taken from five surfaces (refrigeration, knife, cutting board, serving board, and working board) in 20 food trucks in Poland. In 13 food trucks, the visual assessment of hygiene was very good or good, but in 6 FTs, TVC was found to exceed log 3 CFU/100 cm2 on various surfaces. The assessment of surface hygiene using various methods in the food trucks did not demonstrate the substitutability of culture methods. PetrifilmTM tests were shown to be a convenient and reliable tool for the monitoring of mobile catering hygiene. No correlation was found between the subjective visual method and the measurement of adenosine 5-triphosphate. In order to reduce the risk of food infections caused by bacteria in food trucks, it is important to introduce detailed requirements for the hygiene practices used in food trucks, including techniques for monitoring the cleanliness of surfaces coming into contact with food, in particular cutting boards and work surfaces. Efforts should be focused on introducing mandatory, certified training for food truck personnel in the field of microbiological hazards, appropriate methods of hygienisation, and hygiene monitoring.

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor.

Pallet pose estimation is one of the key technologies for automated fork pickup of driverless industrial trucks. Due to the complex working environment and the enormous amount of data, the existing pose estimation approaches cannot meet the working requirements of intelligent logistics equipment in terms of high accuracy and real time. A point cloud data-driven pallet pose estimation method using an active binocular vision sensor is proposed, which consists of point cloud preprocessing, Adaptive Gaussian Weight-based Fast Point Feature Histogram extraction and point cloud registration. The proposed method overcomes the shortcomings of traditional pose estimation methods, such as poor robustness, time consumption and low accuracy, and realizes the efficient and accurate estimation of pallet pose for driverless industrial trucks. Compared with traditional Fast Point Feature Histogram and Signature of Histogram of Orientation, the experimental results show that the proposed approach is superior to the above two methods, improving the accuracy by over 35% and reducing the feature extraction time by over 30%, thereby verifying the effectiveness and superiority of the proposed method.

Characterizing Operating Condition-Based Formaldehyde Emissions of Light-Duty Diesel Trucks in China Using a PEMS-HCHO System.

Formaldehyde (HCHO) plays a critical role in atmospheric photochemistry and public health. While existing studies have suggested that vehicular exhaust is an important source of HCHO, the operating condition-based diesel truck HCHO emission measurements remain severely limited due to the limited temporal resolution and accuracy of measurement techniques. In this study, we characterized the second-by-second HCHO emissions from 29 light-duty diesel trucks (LDDTs) in China over dynamometer and real-world driving tests using a portable online HCHO emission measurement system (PEMS-HCHO), considering various operating conditions. Our results suggested that the HCHO emissions from LDDTs might be underestimated by the widely used offline DNPH-HPLC method. The HCHO emissions at a 200 s cold start from China V LDDT can be up to 50 mg/start. Different driving conditions over dynamometer and real-world driving tests led to a 2-4 times difference in the HCHO emission factors (EFs). Under real-world hot-running conditions, the HCHO EFs of China III, IV, V, and VI LDDTs were 43.5 ± 35.7, 10.6 ± 14.2, 8.8 ± 5.1, and 3.2 ± 1.2 mg/km, respectively, which significantly exceeded the latest California low emission vehicle III HCHO emission standard (2.5 mg/km). These findings highlighted the significant impact of vehicle operating conditions on HCHO emissions and the urgency of regulating HCHO emissions from LDDTs in China.