Materials Challenges in the Electric Vehicle Transition.

The ongoing transition toward electric vehicles (EVs) is changing materials used for vehicle production, of which the consequences for the environmental performance of EVs are not well understood and managed. We demonstrate that electrification coupled with lightweighting of automobiles will lead to significant changes in the industry's demand not only for battery materials but also for other materials used throughout the entire vehicle. Given the automotive industry's substantial consumption of raw materials, changes in its material demands are expected to trigger volatilities in material prices, consequently impacting the material composition and attractiveness of EVs. In addition, the materials recovered during end-of-life recycling of EVs as the vehicle fleet turns over will impact recycled material supplies both positively and negatively, impacting material availabilities and the economic incentive to engage in recycling. These supply chain impacts will influence material usage and the associated environmental performance of not only the automotive sector but also other metal-heavy industries such as construction. In light of these challenges, we propose the need for new research to understand the dynamic materials impacts of the EV transition that encompasses its implications on EV adoption and fleet life cycle environmental performance. Effectively coordinating the coevolution of material supply chains is crucial for making the sustainable transition to EVs a reality.

Comparative analysis of real-world vehicular emissions from BS-IV and BS-VI cars in India.

This study investigates real-world carbon dioxides (CO2) and nitrogen oxides (NOx) emissions from diesel (Bharat Stage-IV (BS-IV)) and petrol/gasoline (BS-IV and BS-VI) cars in Indian driving conditions using a portable emission measurement system (PEMS). The paired sample t-test revealed a significant difference ( p < 0.05) in NOx and CO2 emissions among the three types of cars, except for CO2 emissions ( p > 0.05) between BS-IV petrol and BS-VI petrol cars. The highest NOx emission rates were observed in all car types during acceleration (> 1 m/s2) and deceleration (- 2 m/s2). CO2 emission rates were also high during acceleration (> 1 m/s2) for all car types. At low speeds (around 20 kmph), all car types had low emissions of CO2 and NOx, with acceleration and deceleration rates ranging from - 0.5 to 0.5 m/s2. BS-IV diesel cars emit significantly higher NOx emissions compared to petrol cars, especially at vehicle-specific power (VSP) bin 0 (deceleration to idling mode) and during VSP bin 7 (acceleration mode). BS-IV diesel cars emit 228% and 530% higher NOx emissions than BS-IV and BS-VI petrol cars at VSP bins 0 and 7, respectively. CO2 emissions from BS-VI petrol cars were 10% lower than those from BS-IV petrol cars across all VSP bins, indicating moderate reductions. Furthermore, diesel cars emit 140% less CO2 emissions than petrol cars across various VSP bins. The findings highlight the need for cleaner technologies and responsible driving practices to address vehicular emission concerns.

Chapitre 3. Identité décentralisée et biométrie dans l'automobile.

The car manufacturers continue their offer of mobility services around a customer who is no longer only owner of a vehicle but also simple temporary user. To improve the customer experience, we need to identify the real driver by using decentralized identity on the blockchain, coupled with a biometric system.In this article, based on the experience of a concrete project, we have evaluated the several biometrical methods for capturing information and their reliability in the automotive industry. We will share the lesson learned and the remaining tasks. This elegant means of identifying and exchanging data across customer journeys will open new opportunities between stakeholders. This collaborative co-creation will constitute a digital transformation in the interactions within an ecosystem.

The impact of vision loss on attitudes toward autonomous vehicles: A vision-centric analysis.

SIGNIFICANCE: Autonomous vehicles (AVs) have the promise to be an alternative transportation solution for those with vision loss. However, the impact of vision loss on the perceptions and concerns of AVs is unknown. This study therefore examined whether AVs are perceived differently by blind, visually impaired (VI), and normally sighted people. PURPOSE: This study compared the perceptions of AVs among the blind, VI, and normally sighted. METHODS: Participants' opinions on four perception measures (general opinion, trust, impact on quality of life, and intention to use AVs) and nine concerns regarding AVs were measured. The survey was administered to 51 normally sighted, 68 VI, and 65 blind participants. Analyses of covariance assessed whether the four perception measures and nine concerns varied by vision status (normal vision, VI, blind) and driving status (driver, nondriver). Univariate correlations and multiple regression analyses identified associations and predictors of AV perceptions and concerns from demographic, mood, cognition, travel behavior, and vision measures, which included visual acuity, contrast sensitivity, and visual field. RESULTS: The blind (p<0.001), VI (p<0.001), and nondrivers (p<0.001) showed a greater intention to use AVs compared with those with normal vision and drivers. Similar findings were found for the other perception measures. As visual acuity, contrast sensitivity, and visual field extent declined, positivity toward AVs increased (p<0.001). Visual field extent best predicted general opinion and trust in AVs, whereas driving measures were the best predictors of impact on quality of life and intention to use AVs. Concerns about AVs showed no differences based on vision (p=0.94) or driving (p=0.63) status. CONCLUSIONS: Individuals with vision loss expressed more acceptance of AVs despite their concerns. How positive someone is toward AVs appears to be dependent on their visual field extent and driving status.

The Effect of Changing the Angle of the Passenger Car Seat Backrest on the Head Trajectories of the 50th Percentile Male Dummy.

The aim of the study is to compare the head displacement of the KPSIT C50 dummy during a frontal collision at a speed of 20 km/h, along with the change in the angle of the car seat backrest. Passenger car manufacturers recommend setting the backrest angle of the car seat between 100 and 125 degrees. It should be noted that the driver's position is of great importance in the event of a collision injury. In the event of a rear-end collision, the position of the headrest of the car seat is an element that affects the degree of the driver's injuries. In extreme cases, incorrect positioning of the headrest, even at low speed, can lead to serious injuries to the cervical spine and even death. The article is part of a large-scale study on low-speed crash testing. The research problem concerned the influence of the seat backrest angle on the head displacement during a low-speed collision. The article compares the displacement of the head of the KPSIT C50 dummy during a series of crash tests, where the angle of the car seat backrest was changed. On the basis of the research, it was found that the optimal angle of the car seat backrest is 110 degrees. In addition, a preliminary analysis of the displacements of the dummy's head showed a high risk of whiplash injury in people sitting in a fully reclined seat.

Electromagnetic force from electric vehicles: Potential electromagnetic interference source for subcutaneous implantable defibrillator.

BACKGROUND: Electromagnetic interference (EMI) encompasses electromagnetic field signals that can be detected by a device's circuitry, potentially resulting in adverse effects such as inaccurate sensing, pacing, device mode switching, and defibrillation. EMI may impact the functioning of Cardiac Implantable Electronic Devices (CIEDs) and lead to inappropriate therapy. METHOD: An experimental measuring device, a loop antenna mimicking the implantable cardioverted defibrillator (ICD) antenna, was developed, and validated at the US Food and Drug Administration (FDA) and sent to Wright State University for testing. Two sets of measurements were conducted while the vehicle was connected to a 220-Volt outlet with charging at ON and OFF. Each measurement set involved three readings at various locations, with the antenna oriented in three different positions to account for diverse patient postures. The experiment utilized a Tesla Model 3 electric vehicle (EV), assessing scenarios both inside and outside the car, including the driver's seat, driver's seat floor, passenger's seat, rear seat, rear seat floor, cup holder, charging port (car), and near the charging station. RESULTS: The detected voltage (max 400 to 504 millivolts) around the cup holder inside the car differed from all other measurement scenarios. CONCLUSION: The investigation highlights the identification of EMI signals originating from an EV) that could potentially interrupt the functionality of a Subcutaneous Implantable Cardioverter-Defibrillator (S-ICD). These signals fell within the R-wave Spectrum of 30-300 Hz. Further in-vivo studies are essential to determine accurately the level of interference between S-ICDs and EMI from Electric Vehicles.

Adjusting vehicle secondary safety ratings to account for crash-avoidance technology fitment using real-world crash and injury data.

OBJECTIVES: Publicizing safety ratings of vehicles can motivate manufacturers to prioritize safety and help consumers choose safer vehicles, leading to safer fleets. The benefits of primary safety technologies that prevent crash occurrence are not currently incorporated in current ratings in a way that values their safety benefits consistently. We aimed to propose a method for assigning weights for each safety technology to account for established safety benefits using published effectiveness and prevalence from real-life data. METHODS: To illustrate this method, we present a worked example calculated using crash and injury data from Australia and New Zealand. The method proposed attenuates the weights for given safety technologies where two or more safety technologies fitted to the same vehicle are effective for the same types of crashes. RESULTS: In the worked example using Australasian data, large SUVs were estimated to have the largest safety increment from the fitment of all the technologies considered compared to vehicles without these primary safety technologies, with an almost 17% reduction in crash occurrence. Cars with all the technologies fitted had estimated average crash reduction of between 11% and 12%. CONCLUSIONS: Different market groups have different crash patterns, so the safety attributable to safety technology fitment differs at the market group level. This study presents an approach for providing a summary measure of crash avoidance according to the fitment of safety technologies. If this measure is combined with an estimate of secondary safety (whether derived from existing crash and injury data or from new car crash assessment programs), the combined estimate then represents the important elements of safety provided by the vehicle. The methods presented here form a rational basis for assigning safety ratings to represent the benefits of swiftly developing safety technologies.

Mechanism of PGMs capture from spent automobile catalyst by copper from waste printed circuit boards with simultaneous pollutants transformation.

The traditional pyrometallurgical recycling of nano-sized platinum group metals (PGMs) from spent automotive catalysts (SACs) is an energy-intensive process that requires the addition of large quantities of copper capture and slag-forming reagents. Similarly, pyro-recycling of valuable metals from waste printed circuit boards (WPCBs) is also an energy- and reagent-intensive process that and carries a risk of pollution emissions. Based on the complementarity of composition and similarity of recycling process, synergistic pyro-recycling of SACs and WPCBs allow copper in WPCBs to capture PGMs in SACs and oxides from two waste form slag jointly, which offers benefits of enhanced metal recovery, reduced reagent and energy consumption, and suppressed pollutant emissions. However, the mechanisms of PGMs capture and pollutant transformation in co-smelting remain unknown. Here, we investigated the sub-processes mechanisms of slag formation, brominates fixation, multi-metal distribution and kinetic settlement. Oxides in both wastes support SiO2-Al2O3-CaO slag formation with low melting point and viscosity, where CaO suppresses the emission of brominated pollutants. Copper (50-100 µm) from WPCBs facilitates nano-sized PGMs in SACs recovery through capture and settlement. The results of demonstration experiments indicated a recovery rate of 94.6 %, 96.8 %, 97.2 %, and 98.1 % for Cu, Pt, Pd, and Rh, respectively, with a debromination efficiency exceeding 98 %. The theoretical analysis provides support for the establishment of a synergistic pyro-recycling process for SACs and WPCBs and provides insights into the potential for a greener and more efficient co-recycling of multi urban mines.

Vehicle Lane-Changing scenario generation using time-series generative adversarial networks with an Adaptative parameter optimization strategy.

Connected and automated vehicles (CAVs) hold promise for enhancing transportation safety and efficiency. However, their large-scale deployment necessitates rigorous testing across diverse driving scenarios to ensure safety performance. In order to address two challenges of test scenario diversity and comprehensive evaluation, this study proposes a vehicle lane-changing scenario generation method based on a time-series generative adversarial network (TimeGAN) with an adaptive parameter optimization strategy (APOS). With just 13.3% of parameter combinations tested, we successfully trained a satisfactory TimeGAN and generate a substantial number of lane-changing scenarios. Then, the generated scenarios were evaluated for diversity, fidelity, and utility, demonstrating their effectiveness in capturing a wide range of driving situations. Furthermore, we employed a Lane-Changing Risk Index (LCRI) to identify the rare adversarial cases in scenarios. Compared to real scenarios, our approach generates 27 times more adversarial cases with 1.8 times higher average risk, highlighting its potential for uncovering critical safety vulnerabilities. This study paves the way for more comprehensive and effective CAV testing, ultimately contributing to safer and more reliable autonomous driving technologies.

Classification of autonomous vehicle crash severity: Solving the problems of imbalanced datasets and small sample size.

Only a few researchers have shown how environmental factors and road features relate to Autonomous Vehicle (AV) crash severity levels, and none have focused on the data limitation problems, such as small sample sizes, imbalanced datasets, and high dimensional features. To address these problems, we analyzed an AV crash dataset (2019 to 2021) from the California Department of Motor Vehicles (CA DMV), which included 266 collision reports (51 of those causing injuries). We included external environmental variables by collecting various points of interest (POIs) and roadway features from Open Street Map (OSM) and Data San Francisco (SF). Random Over-Sampling Examples (ROSE) and the Synthetic Minority Over-Sampling Technique (SMOTE) methods were used to balance the dataset and increase the sample size. These two balancing methods were used to expand the dataset and solve the small sample size problem simultaneously. Mutual information, random forest, and XGboost were utilized to address the high dimensional feature and the selection problem caused by including a variety of types of POIs as predictive variables. Because existing studies do not use consistent procedures, we compared the effectiveness of using the feature-selection preprocessing method as the first process to employing the data-balance technique as the first process. Our results showed that AV crash severity levels are related to vehicle manufacturers, vehicle damage level, collision type, vehicle movement, the parties involved in the crash, speed limit, and some types of POIs (areas near transportation, entertainment venues, public places, schools, and medical facilities). Both resampling methods and three data preprocessing methods improved model performance, and the model that used SMOTE and data-balancing first was the best. The results suggest that over-sampling and the feature selection method can improve model prediction performance and define new factors related to AV crash severity levels.

Analysis on congestion mechanism of CAVs around traffic accident zones.

Unexpected traffic accidents cause traffic congestion and aggravate the unsafe situation on the roadways. Reducing the impact of such congestion by introducing Connected and Autonomous Vehicles (CAVs) into the traditional traffic flow is possible. It requires estimating the incident's duration and analyzing the incident's impact area to determine the appropriate strategy. To guide the driver in making efficient and accurate judgments and avoiding secondary traffic congestion, the Cooperative Adaptive Cruise Control (CACC) model with dynamic safety distance and the Intelligent Driver Model (IDM) based on the safety potential field theory are introduced to build the evolution model of accidental traffic congestion under diversion interference and non-interference. The Huatao Interchange section of the Inner Ring Highway in the Banan District of Chongqing, China, was selected as the test section for simulating mixed traffic flow under different CAVs permeability (Pc). The relationship between the evacuation time, evacuation traffic volume, and the accident impact degree index (including the farthest queue length and accident duration) under the diversion intervention scenario was analyzed, respectively. The results of the study indicate that the higher the penetration of CAVs, the more significant the improvement in traffic flow occupancy, flow, and speed. Diversion interventions reduce congestion, about 50 % of the duration without interventions, when Pc ≤ 80 %. The traffic volume of diversion interference is non-linearly positively correlated with the maximum queue length, and the earlier the interference time, the stronger the positive correlation. The negative correlation between the interference time and queue length is weak at low evacuation traffic volume. With the increase in evacuation traffic volume, the influence of evacuation time on queue length becomes stronger. The maximum queue length value interval under different conditions is [348 m, 3140 m], and the shortest evacuation time is [1649 s, 2834 s]. The traffic flow data obtained from the simulation are imported into the episodic traffic congestion evolution model. The congestion evaluation indexes are calculated under non-interference and interference measures and compared with the simulation results. The maximum relative error is within 5.38 %. The results can be of great significance in relieving congestion caused by traffic accidents and promptly restoring road capacity.

Law compliance decision making for autonomous vehicles on highways.

As autonomous driving advances, autonomous vehicles will share the road with human drivers. This requires autonomous vehicles to adhere to human traffic laws under safe conditions. Simultaneously, when confronted with dangerous situations, autonomous driving should also possess the capability to deviate from traffic laws to ensure safety. However, current autonomous vehicles primarily prioritize safety and collision avoidance in their decision-making and planning. This may lead to misunderstandings and distrust from human drivers in mixed traffic flow, and even accidents. To address this, this paper proposes a decoupled hierarchical framework for compliance safety decision-making. The framework primarily consists of two layers: the decision-making layer and the motion planning layer. In the decision-making layer, a candidate behavior set is constructed based on the scenario, and a dual layer admission assessment is utilized to filter out unsafe and non-compliant behaviors from the candidate sets. Subsequently, the optimal behavior is selected as the decision behavior according to the designed evaluation metrics. The decision-making layer ensures that the vehicle can meet lane safety requirements and comply with static traffic laws. In the motion planning layer, the surrounding vehicles and the road are modeled as safety potential fields and traffic laws potential fields. Combining the optimal decision behavior, they are incorporated into the cost function of the model predictive control to achieve compliant and safe trajectory planning. The planning layer ensures that the vehicle meets trajectory safety requirements and complies with dynamic traffic laws under safe conditions. Finally, four typical scenarios are used to evaluate the effectiveness of the proposed method. The results indicate that the proposed method can ensure compliance in safe conditions while also temporarily deviating from traffic laws in emergency situations to ensure safety.

Drinking and driving: A systematic review of the impacts of alcohol consumption on manual and automated driving performance.

INTRODUCTION: Almost a third of car accidents involve driving after alcohol consumption. Autonomous vehicles (AVs) may offer accident-prevention benefits, but at current automation levels, drivers must still perform manual driving tasks when automated systems fail. Therefore, understanding how alcohol affects driving in both manual and automated contexts offers insight into the role of future vehicle design in mediating crash risks for alcohol-impaired driving. METHOD: This study conducted a systematic review on alcohol effects on manual and automated (takeover) driving performance. Fifty-three articles from eight databases were analyzed, with findings structured based on the information processing model, which can be extended to the AV takeover model. RESULTS: The literature indicates that different Blood Alcohol Concentration (BAC) levels affect driving skills essential for traffic safety at various information processing stages, such as delayed reacting time, impaired cognitive abilities, and hindered execution of driving tasks. Additionally, the driver's driving experience, drinking habits, and external driving environment play important roles in influencing driving performance. CONCLUSIONS: Future work is needed to examine the effects of alcohol on driving performance, particularly in AVs and takeover situations, and to develop driver monitoring systems. PRACTICAL APPLICATIONS: Findings from this review can inform future experiments, AV technology design, and the development of driver state monitoring systems.

Perceptions of vulnerable roadway users on autonomous vehicle regulations.

INTRODUCTION: Development and implementation of autonomous vehicle (AV) related regulations are necessary to ensure safe AV deployment and wide acceptance among all roadway users. Assessment of vulnerable roadway users' perceptions on AV regulations could inform policymakers the development of appropriate AV regulations that facilitate the safety of diverse users in a multimodal transportation system. METHOD: This research evaluated pedestrians' and bicyclists' perceptions on six AV regulations (i.e., capping AV speed limit, operating AV in manual mode in the sensitive areas, having both pilot and co-pilot while operating AVs, and three data-sharing regulations). In addition, pedestrians' and bicyclists' perceptions of testing AVs in public streets were evaluated. Statistical testing and modeling techniques were applied to accomplish the research objectives. RESULTS: Compared to the other AV regulations assessed in this research, strong support for AV-related data sharing regulations was identified. Older respondents showed higher approval of AV testing on public roadways and less support for regulating AVs. AV technology familiarity and safe road sharing perceptions with AVs resulted in lower support for AV regulations. CONCLUSIONS: Policymakers and AV technology developers could develop effective educational tools/resources to inform pedestrians and bicyclists about AV technology reliability and soften their stance, especially on AV regulations, which could delay technology development. PRACTICAL APPLICATIONS: The findings of this research could be used to develop informed AV regulations and develop policies that could improve pedestrians' and bicyclists' attitudes/perceptions on regulating AVs and promoting AV technology deployments.

Factors Affecting the Situational Awareness of Armored Vehicle Occupants.

In the field of armored vehicles, up to 70% of accidents are associated with low levels of situational awareness among the occupants, highlighting the importance of situational awareness in improving task performance. In this study, we explored the mechanisms influencing situational awareness by simulating an armored vehicle driving platform with 14 levels of experimentation in terms of five factors: experience, expectations, attention, the cueing channel, and automation. The experimental data included SART and SAGAT questionnaire scores, eye movement indicators, and electrocardiographic and electrodermal signals. Data processing and analysis revealed the following conclusions: (1) Experienced operators have higher levels of situational awareness. (2) Operators with certain expectations have lower levels of situational awareness. (3) Situational awareness levels are negatively correlated with information importance affiliations and the frequency of anomalous information in non-primary tasks. (4) Dual-channel cues lead to higher levels of situational awareness than single-channel cues. (5) Operators' situational awareness is lower at high automation levels.

Growable design of passenger vehicle interior space based on FAHP and FQFD.

The increasingly shortened development cycle of smart vehicles has led to a qualitative shift in the nature of automotive products. Growing spatial design of vehicle interiors can effectively satisfy users' personalisation preferences and increase their willingness to buy, as well as mitigating the environmental pollution caused by the problem of rapid replacement. Considering the subjectivity and uncertainty of users' emotional needs, this study adopts the FAHP method to comprehensively analyse and rank the SET series of factors, then combines the grey correlation method with the correlation analysis of the areas related to the interior space of the automobile, constructs the sample of the interior space of the automobile and extracts the kansei words of the space sample. Intentional vocabulary mean scores were calculated to factor analyses through kansei engineering, next the fuzzy QFD quality house was built to make affective semantic design associations and derive design weights, which are then used to guide the design and ultimately realise the design of a dynamic automotive interaction scenario. The results of the study show that the integration of different theories can reduce the uncertainties in accessing users' emotional needs. At the same time, it can provide systematic guidance for the interaction design of a growable automobile in terms of multiple dimensions of interior space connectivity, spatial layout, and perceptual experience, as well as provide valuable suggestions for the subsequent development of interior spaces.

A matched case-control analysis of autonomous vs human-driven vehicle accidents.

Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles' levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous' versus Human-Driven Vehicles' accidents. The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios. However, accidents involving Advanced Driving Systems occur more frequently than Human-Driven Vehicle accidents under dawn/dusk or turning conditions, which is 5.25 and 1.98 times higher, respectively. Our research reveals the accident risk disparities between Autonomous Vehicles and Human-Driven Vehicles, informing future development in Autonomous technology and safety enhancements.

Two hybrid flow shop scheduling lines with assembly stage and compatibility constraints.

Two hybrid flow shop scheduling lines must be coordinated to assemble batches of terminated products at their last stage. Each product is thus composed of two jobs, each produced in one of the lines. The set of jobs is to be processed in a series of stages to minimize the makespan of the scheduling, but jobs forming a product must arrive at the assembly line simultaneously. We propose a mixed integer linear programming model. Then, based on the model, we propose a pull-matheuristic algorithm. Finally, we present two metaheuristics, a greedy randomized adaptive search procedure and a biased random key genetic algorithm, and compare all the methodologies with real-based instances of a production scheduling problem in the automobile manufacturing industry. The greedy algorithm yields high-quality solutions, while the genetic one offers the best computational times.

Highly smooth and parameter independent obstacle avoidance method for autonomous vehicle with velocity-varying obstacle.

One of the primary challenges for autonomous vehicle (AV) is planning a collision-free path in dynamic environment. It is a tricky task for achieving high-performance obstacle avoidance with velocity-varying obstacle. To solve this problem, a highly smooth and parameter independent obstacle avoidance method for autonomous vehicle with velocity-varying obstacle (HSPI-OAM) is presented in this work. The proposed method uses the virtual collision point model to accurately design the desired acceleration, which makes the obtained path highly smooth. At the same time, the method gets rid of the dependence on parameter adjustment and has strong adaptability to different environments. The simulation is implemented on the Matlab-Carsim co-simulation platform, and the simulation results show that the path planned by HSPI-OAM has good performance for obstacle with acceleration.