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Resilient object detection for autonomous vehicles: Integrating deep learning and sensor fusion in adverse conditions
2025-03-26 - Thottempudi, Pardhu; Jambek, Asral Bin Bahari; Kumar, Vijay; Acharya, Biswaranjan; Moreira, Fernando
Autonomous vehicles (AVs) rely on advanced object detection systems to ensure safe navigation, especially under adverse weather conditions that can impair sensor visibility and introduce detection challenges. This manuscript provides a comprehensive analysis of state-of-the-art methodologies, focusing on deep learning frameworks, multi-sensor fusion techniques, and specialized datasets designed for AV object detection across various environmental conditions. We categorize approaches based on accuracy, computational efficiency, and resilience to challenging weather scenarios, offering insights into the strengths and limitations of each technique. Additionally, widely used datasets, such as KITTI and Waymo, along with synthetic and real-time datasets, are evaluated to assess their impact on detection accuracy in complex scenarios. While deep learning models demonstrate high accuracy, the integration of sensor fusion and transfer learning techniques further enhances robustness and adaptability. Our findings emphasize the importance of developing weather-resilient AV perception systems and provide recommendations for advancing object detection frameworks in autonomous driving applications.
Beyond image: Evaluating the perceived effectiveness of workplace mental health programs in the insurance sector
2025-01-18 - Barbosa, Isabel Cristina Pereira; Real, Elizabeth
Background
Mental health programs in the workplace have gained increasing attention as organizations strive to support employee well-being. However, the effectiveness and reception of these initiatives from the employee perspective still need to be studied.
Methods
A qualitative study used 30 semi-structured interviews with individuals from large insurance companies in Portugal. It focused on their perceptions of workplace mental health programs, stress, and time management training. The participants ranged in age from 25 to 55 years (average 38), with a slightly more significant proportion of women, reflecting the gender distribution in the Portuguese insurance sector. The sample had an average of 10 years of industry experience, from entry-level to middle-management roles. Thematic analysis with an audit trail and thick description was used to analyse data.
Results
The findings revealed a significant disconnect between the intended benefits of these programs and employee experiences. Many viewed the initiatives as primarily beneficial for the company’s image rather than employee well-being. A common concern was that workload remained unchanged after training, leading employees to prioritize work over program participation. Additional key findings included generational differences in program perception, with younger employees expressing more frustration over the lack of follow-through. Most participants reported that initiatives failed to address the root causes of stress. More than half of the interviewees felt a lingering stigma around discussing mental health at work, whereas a majority mentioned increased stress due to rapid industry digitalization. Notably, many participants perceived a significant gap between upper management’s promotion of mental health initiatives and their understanding of daily employee challenges.
Conclusions
These findings highlight a critical gap between implementing workplace mental health programs and their perceived value among employees. Organizations need to reevaluate their mental health initiatives to ensure that they provide tangible benefits and address underlying workplace stressors. Future research should explore strategies to better align mental health programs with the insurance sector’s employee needs and work realities.