Soiling detection for automotive cameras is a crucial part of advanced driver assistance systems to make them more robust to external conditions like weather, dust, etc. In this paper, we regard the soiling detection as a semantic segmentation problem. We provide a comprehensive comparison of popular segmentation methods and show their superiority in performance while comparing them to tile-level classification approaches. Moreover, we present an extensive analysis of the Woodscape dataset showing that the original dataset contains a data-leakage and imprecise annotations. To address these problems, we create a new data subset, which, despite being much smaller, provides enough information for the segmentation method to reach comparable results in a much shorter time.

Pedestrian fatalities continue to rise in the United States, driven by factors such as human distraction, increased vehicle size, and complex traffic environments. Advanced Driver Assistance Systems (ADAS) offer a promising avenue for improving pedestrian safety by enhancing driver awareness and vehicle responsiveness. This study conducts a comprehensive data-driven analysis utilizing the Fatality Analysis Reporting System (FARS) to quantify the effectiveness of specific ADAS features like Pedestrian Automatic Emergency Braking (PAEB), Forward Collision Warning (FCW), and Lane Departure Warning (LDW), in lowering pedestrian fatalities. By linking vehicle specifications with crash data, we assess how ADAS performance varies under different environmental and behavioral conditions, such as lighting, weather, and driver/pedestrian distraction. Results indicate that while ADAS can reduce crash severity and prevent some fatalities, its effectiveness is diminished in low-light and adverse weather. The findings highlight the need for enhanced sensor technologies and improved driver education. This research informs policymakers, transportation planners, and automotive manufacturers on optimizing ADAS deployment to improve pedestrian safety and reduce traffic-related deaths.

Advanced Driver Assistance Systems (ADAS) significantly enhance road safety by detecting potential collisions and alerting drivers. However, their reliance on expensive sensor technologies such as LiDAR and radar limits accessibility, particularly in low- and middle-income countries. Machine learning-based ADAS (ML-ADAS), leveraging deep neural networks (DNNs) with only standard camera input, offers a cost-effective alternative. Critical to ML-ADAS is the collision avoidance feature, which requires the ability to detect objects and estimate their distances accurately. This is achieved with specialized DNNs like YOLO, which provides real-time object detection, and a lightweight, detection-wise distance estimation approach that relies on key features extracted from the detections like bounding box dimensions and size. However, the robustness of these systems is undermined by security vulnerabilities in object detectors. In this paper, we introduce ShrinkBox, a novel backdoor attack targeting object detection in collision avoidance ML-ADAS. Unlike existing attacks that manipulate object class labels or presence, ShrinkBox subtly shrinks ground truth bounding boxes. This attack remains undetected in dataset inspections and standard benchmarks while severely disrupting downstream distance estimation. We demonstrate that ShrinkBox can be realized in the YOLOv9m object detector at an Attack Success Rate (ASR) of 96%, with only a 4% poisoning ratio in the training instances of the KITTI dataset. Furthermore, given the low error targets introduced in our relaxed poisoning strategy, we find that ShrinkBox increases the Mean Absolute Error (MAE) in downstream distance estimation by more than 3x on poisoned samples, potentially resulting in delays or prevention of collision warnings altogether.

Leveraging a newly released open dataset of Lane Keeping Assist (LKA) systems from production vehicles, this paper presents the first comprehensive empirical analysis of real-world LKA performance. Our study yields three key findings: (i) LKA failures can be systematically categorized into perception, planning, and control errors. We present representative examples of each failure mode through in-depth analysis of LKA-related CAN signals, enabling both justification of the failure mechanisms and diagnosis of when and where each module begins to degrade; (ii) LKA systems tend to follow a fixed lane-centering strategy, often resulting in outward drift that increases linearly with road curvature, whereas human drivers proactively steer slightly inward on similar curved segments; (iii) We provide the first statistical summary and distribution analysis of environmental and road conditions under LKA failures, identifying with statistical significance that faded lane markings, low pavement laneline contrast, and sharp curvature are the most dominant individual factors, along with critical combinations that substantially increase failure likelihood. Building on these insights, we propose a theoretical model that integrates road geometry, speed limits, and LKA steering capability to inform infrastructure design. Additionally, we develop a machine learning-based model to assess roadway readiness for LKA deployment, offering practical tools for safer infrastructure planning, especially in rural areas. This work highlights key limitations of current LKA systems and supports the advancement of safer and more reliable autonomous driving technologies.

Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems Hannah Musau a,, Nana Kankam Gyimah a, Judith Mwakalonge a, Gurcan Comert b, Saidi Siuhi a a Department of Engineering, South Carolina State University, Orangeburg, South Carolina, USA, 29117 b Department of Computational Engineering and Data Science, North Carolina A&T State University, Greensboro, North Carolina, US, 27411Abstract Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This study examines driver perceptions, knowledge sources, and usage patterns of ADAS in passenger vehicles. A nationwide survey collected data from a diverse sample of U.S. drivers. Machine learning models predicted ADAS adoption, with SHAP (SHapley Additive Explanations) identifying key influencing factors. Findings indicate that higher trust levels correlate with increased ADAS usage, while concerns about reliability remain a barrier. Findings emphasize the influence of socioeconomic, demographic, and behavioral factors on ADAS adoption, offering guidance for automakers, policymakers, and safety advocates to improve awareness, trust, and usability. Introduction Human factors are the leading cause of road crashes, contributing to over 90% of incidents either alone or alongside failures in vehicles or infrastructure [1].

Advanced Driver Assistance Systems (ADAS) are designed with the main purpose of increasing the safety and comfort of vehicle occupants. Most of current computer vision-based ADAS perform detection and tracking tasks quite successfully under regular conditions, but are not completely reliable, particularly under adverse weather and changing lighting conditions, neither in complex situations with many overlapping objects. In this work we explore the use of hyperspectral imaging (HSI) in ADAS on the assumption that the distinct near infrared (NIR) spectral reflectances of different materials can help to better separate the objects in a driving scene. In particular, this paper describes some experimental results of the application of fully convolutional networks (FCN) to the image segmentation of HSI for ADAS applications. More specifically, our aim is to investigate to what extent the spatial features codified by convolutional filters can be helpful to improve the performance of HSI segmentation systems. With that aim, we use the HSI-Drive v1.1 dataset, which provides a set of labelled images recorded in real driving conditions with a small-size snapshot NIR-HSI camera. Finally, we analyze the implementability of such a HSI segmentation system by prototyping the developed FCN model together with the necessary hyperspectral cube preprocessing stage and characterizing its performance on an MPSoC.

Visual Saliency refers to the innate human mechanism of focusing on and extracting important features from the observed environment. Recently, there has been a notable surge of interest in the field of automotive research regarding the estimation of visual saliency. While operating a vehicle, drivers naturally direct their attention towards specific objects, employing brain-driven saliency mechanisms that prioritize certain elements over others. In this investigation, we present an intelligent system that combines a drowsiness detection system for drivers with a scene comprehension pipeline based on saliency. To achieve this, we have implemented a specialized 3D deep network for semantic segmentation, which has been pretrained and tailored for processing the frames captured by an automotive-grade external camera. The proposed pipeline was hosted on an embedded platform utilizing the STA1295 core, featuring ARM A7 dual-cores, and embeds an hardware accelerator. Additionally, we employ an innovative biosensor embedded on the car steering wheel to monitor the driver drowsiness, gathering the PhotoPlethysmoGraphy (PPG) signal of the driver. A dedicated 1D temporal deep convolutional network has been devised to classify the collected PPG time-series, enabling us to assess the driver level of attentiveness. Ultimately, we compare the determined attention level of the driver with the corresponding saliency-based scene classification to evaluate the overall safety level. The efficacy of the proposed pipeline has been validated through extensive experimental results.

A few bright spots remain in the autonomous vehicle industry even amid macroeconomic headwinds that have nearly shut off the spigot of venture capital and led to further consolidation. Helm.ai, a startup developing software designed for advanced driver assistance systems, autonomous driving and robotics, is one of them. The Menlo Park, California-based startup recently raised $31 million in a Series C round led by Freeman Group, just one year after it snagged $26 million in venture funding. This latest round, which included ACVC Partners, Amplo and strategic investors Honda Motor Co., Goodyear Ventures and Sungwoo Hitech, has pushed Helm.ai's valuation to $431 million. Brandon Freeman, founder of the Freeman Group, is joining the Helm.ai board of directors as part of this financing.

Earlier this month, the National Highway Traffic Safety Administration (NHTSA) released a report documenting crashes involving cars with automated driving components. The report looked at data on Automated Driving Systems (commonly referred to as "self-driving cars") and Advanced Driver Assistance Systems (cars equipped with lane-keeping technology and adaptive cruise control, such as Tesla's Autopilot). The New York Times covered the report's release. A quick scroll through Twitter showed that the public divided: Is this technology something to praise, or something to fear? Ultimately, the NHTSA report, while an essential first step, doesn't leave a clear picture whether self-driving cars will prevent crashes when they arrive in the future.

You're driving and you're bored. Tired of staring at the road, your eyes drift toward the polished touchscreen to the right of your steering wheel--what the auto industry calls your "infotainment" system. First you scroll through its menus to select a pump-me-up playlist; then you use its mapping tool to reroute toward a nearby Starbucks. Sounds like a typical driving experience these days. Sure, you temporarily looked away from the road while tapping through the infotainment system, but that's no big deal.