Accurately estimating the driving range of electric trucks remains a critical challenge due to higher complexity and operational diversity compared to passenger EVs. Factors such as variable payloads, aerodynamic variability from different trailers, and auxiliary power demands significantly impact energy usage. Conventional data-driven models, while effective, frequently struggle to generalize under extreme conditions. This paper introduces a novel Physics-Informed Neural Network (PINN) framework for electric truck range estimation that combines physics-based modeling with data-driven learning. A custom loss function dynamically balances physical constraints and empirical insights using a time-varying parameter. The model prioritizes physics early in training, and then shifts toward data-driven fine-tuning. Experimental results demonstrate that while pure data-driven models achieve higher accuracy under normal conditions, the proposed PINN framework demonstrates superior robustness, particularly with small datasets and in challenging weather and operational scenarios. These findings highlight the potential of PINN - based methods in addressing key challenges in electric truck range estimation.

Reconstructing the geometry and appearance of a given scene is a fundamental task in 3D computer graphics and computer vision. Recently, radiance fields have emerged as a representation of light transport in the scene, allowing, as a byproduct, also to extract 3D geometry solely from multi-view imagery. Initially designed for RGB captures, existing approaches have been extended to other sensor modalities. Among these, transient imaging — measuring the time-of-flight of light at picosecond resolution — has emerged as a promising alternative, offering rich spatio-temporal information to improve reconstruction quality from limited viewpoints and obstructed views. However, its applicability to outdoor scenarios has been highly problematic due to interference from ambient light and the different sensor behavior under high-photon-flux conditions typical of outdoor settings. Addressing this gap, we introduce Transient LASSO, a neural scene reconstruction method operating on raw transient measures of outdoor in-the-wild captures to accurately reconstruct the underlying scene geometry and properties. We demonstrate the effectiveness of our method across a variety of outdoor environments, including complex urban scenes with dense traffic and infrastructure. Finally, we also show the potential use cases of our method for downstream applications such as sensor parameter optimization.

Artificial Intelligence (AI) shows promising applications for the perception and planning tasks in autonomous driving (AD) due to its superior performance compared to conventional methods. However, highly complex AI systems exacerbate the existing challenge of safety assurance of AD. One way to mitigate this challenge is to utilize explainable AI (XAI) techniques. To this end, we present the first comprehensive systematic literature review of explainable methods for safe and trustworthy AD. We begin by analyzing the requirements for AI in the context of AD, focusing on three key aspects: data, model, and agency. We find that XAI is fundamental to meeting these requirements. Based on this, we explain the sources of explanations in AI and describe a taxonomy of XAI. We then identify five key contributions of XAI for safe and trustworthy AI in AD, which are interpretable design, interpretable surrogate models, interpretable monitoring, auxiliary explanations, and interpretable validation. Finally, we propose a conceptual modular framework called SafeX to integrate the reviewed methods, enabling explanation delivery to users while simultaneously ensuring the safety of AI models.

This study investigates the impact of initial contact of drivers with an SAE Level 3 Automated Driving System (ADS) under real traffic conditions, focusing on the Mercedes-Benz Drive Pilot in the EQS. It examines Acceptance, Trust, Usability, and User Experience. Although previous studies in simulated environments provided insights into human-automation interaction, real-world experiences can differ significantly. The research was conducted on a segment of German interstate with 30 participants lacking familiarity with SAE Level 3 ADS. Pre- and post-driving questionnaires were used to assess changes in acceptance and trust. Supplementary metrics included post-driving ratings for usability and user experience. Findings reveal a significant increase in acceptance and trust following the first contact, confirming results from prior studies. Factors such as Performance Expectancy, Effort Expectancy, Facilitating Condition, and Perceived Safety were rated higher after initial contact with the ADS. However, inadequate communication from the ADS to the human driver was detected, highlighting the need for improved communication to prevent misuse or confusion about the operating mode. However, it’s worth noting that most participants already had a high affinity for technology. Although overall reception was positive and showed an upward trend post first contact, the ADS was also perceived as demanding as manual driving. Future research should focus on a more diverse participant sample and include longer or multiple real-traffic trips to understand behavioral adaptations over time.

Despite the presence of the classification task in many different benchmark datasets for perception in the automotive domain, few efforts have been undertaken to define consistent classification requirements. This work addresses the topic by proposing a structured method to generate a classification structure. First, legal categories are identified based on behavioral requirements for the vehicle. This structure is further substantiated by considering the two aspects of collision safety for objects as well as perceptual categories. A classification hierarchy is obtained by applying the method to an exemplary legal text. A comparison of the results with benchmark dataset categories shows limited agreement. This indicates the necessity for explicit consideration of legal requirements regarding perception.