Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
When a new vehicle is being created or developed, many technical parameters that affect dynamic characteristics must be investigated not only on a theoretical level, but also by natural experiments. Especially one of the most important characteristics for a vehicle that can tilt is tire–road contact, which later helps to calculate and simulate different driving conditions in different driving scenarios, applying internal and external forces. This paper presents a unique construction of a three-wheeled tilting vehicle prototype, tire–road contact determination, and evaluation of vehicle behaviour using the Pacejka tire model. To achieve this, the tire and road surface area were investigated. Using the computed method, experimentally determined contact areas were refined and compared with the actual measured. Determined tire–road contact areas were evaluated by applying dynamic external forces for further investigation. Selected a scenario to predict the behavior of a three-wheeled tilting vehicle and the force distribution during tilting, then determined certain vehicle parameters in the static position (load distribution, tire–road contact areas). The inclusion of asymmetric front-left and front-right tire loads under tilt resulted in observable differences in force distribution. The inner front tire unloaded while the outer tire gained load, introducing asymmetry in both lateral and longitudinal forces. This behaviour was not captured in the symmetric model....
While developing child-presence detection with ultra-wideband radar, we observed that trunk-lid motion can mimic in-cabin child activity and cause false alarms. We address this issue with a lightweight, purely time-domain detector that operates on slidingwindow skewness and kurtosis of the radar amplitude stream. Trunk operations give rise to extended, low-variation plateaus in these statistics, whereas child motion remains non-stationary, so that a simple rule based on synchronized stable runs can separate the two classes without learning-based models. The detector is implemented in a streaming fashion using incremental updates, leading to constant per-sample complexity, fixed decision latency and modest memory usage suitable for embedded electronic control units. We evaluate the method on data collected in real in-vehicle environments with diverse trunk operations and child activities, and the results show that the proposed criterion reliably suppresses trunk-induced false alarms without introducing misclassifications. Because it uses only distributional time-domain features from a single multi-channel ultra-wideband radar, the approach is privacy-preserving and readily integrable into existing child-presence detection pipelines....
Industrial policy is in increasing use in both the U.S. and other countries. The auto industry, however, has benefited from industrial policy even during decades when it was out of favor, from NAFTA to the 2009 auto bailouts, IRA’s EV tax credits, and now auto tariffs. If industrial policy should be applied only very selectively, should the auto industry be an industrial policy target today? Now is a dynamic time in the global auto industry. The shift to electric vehicles (EVs) is upending a longstable competitive landscape. China’s indigenous EV automakers have become major producers and exporters in a handful of years. The Chinese government is pervasively involved in its EV industry, while having developed a national monopoly position in global EV supply chains. Adding to security considerations, domestic auto manufacturing offers contingent defense manufacturing capability, while the computerization of vehicles raises new surveillance and cyberattack risks. This article examines today’s U.S. auto industrial policy in international and technological context, reviews the short track record of Biden EV policies, discusses how we should evaluate Trump’s auto industrial policy, and closes with recommendations for where industrial policy would be best focused to support the U.S. auto sector....
We examine the moral dilemma of how autonomous vehicles (AVs) should be programmed to act in unavoidable crash scenarios involving trade-offs between saving one life and saving many. We report results from three experimental studies that investigate individuals’ preferences over alternative AV decision rules in stylized crash scenarios. Across designs, we find robust support for a probabilistic decision rule that assigns passengers and pedestrians equal ex ante chances of survival (a 50:50 rule). This preference persists across different framings and remains salient even when additional probabilistic options are introduced....
The majority of existing research on collision severity focuses on post-collision severity, which is not conducive to collision prevention. This paper proposes a novel method for predicting the severity of potential collisions, aiming to establish a prediction model to predict the potential consequences of collisions before they occur, providing a basis for quantifying driving risk. In developing this model, two key challenges are addressed: how to effectively characterise the severity of potential collisions and how to manage the class imbalance caused by the scarcity of severe collisions. To tackle the first challenge, we introduce a systematic approach to find the most representative features of potential collision severity. For the second challenge, we propose a distribution-preserving resampling method to address the class imbalance. This approach includes two techniques: Remove Redundant Under Sampling (RRUS) and Core Seed-based Synthetic Minority Oversampling Technique (CS-SMOTE), which transform the imbalanced dataset into a balanced one while preserving the distribution characteristics of the original dataset. Finally, using the National Highway Traffic Safety Administration (NHTSA) dataset and the XGBoost algorithm, a potential collision severity prediction model is developed. The results demonstrate that the model achieves a prediction accuracy of over 97.7%, outperforming comparison models developed using other classification algorithms....
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