Road surface condition monitoring is crucial for enhancing transportation safety and efficiency, with applications in autonomous driving and urban infrastructure management. Existing methods often rely on single-camera setups or manual inspections, which are either insufficient for real-time monitoring or labor-intensive. This system focuses on two critical factors: road slope and surface damage, both significantly impacting driving safety and experience, highlighting the need for timely detection. To ensure accuracy and robustness, the system employs a binocular camera for detailed road environment insights and integrates urban sensing techniques. Its hardware deployment processes stereo vision data on embedded platforms, ensuring compatibility with urban IoT networks. This approach surpasses single-camera systems in detecting road surface variations. The research motivation stems from the pressing need to enhance road safety and driving conditions in urban areas. By analyzing binocular camera data and urban sensing technologies, the system offers real-time road condition analysis for effective decision-making. Regarding results, the system showed robust performance in detecting both road slope and surface damage. Slope detection achieved high accuracy with minimal error, and road damage detection reached an overall accuracy of 84%. The system remained stable across diverse conditions, including adverse weather and varying lighting.

The road environment prediction is an essential task for intelligent vehicle. In this study, we provide a flexible system that focuses on freespace detection and road environment prediction to host vehicle. The hardware of this system includes two parts: a binocular camera and a low‐power mobile platform, which is flexible and portable for a variety of intelligent vehicle. We put forward a multiscale stereo matching algorithm to reduce the computing cost of the hardware unit. Based on disparity space and points cloud, we propose a weighted probability grid map to detect freespace region and a state model to describe the road environment. The experiments show that the proposed system is accurate and robust, which indicates that this technique is fully competent for road environment prediction for intelligent vehicle.

MOTIVATION: Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples. RESULTS: We present SomVarIUS, a computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of ∼150 × coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. We demonstrate the utility of SomVarIUS by identifying somatic mutations in formalin-fixed samples, and tracking clonal dynamics of oncogenic mutations in targeted deep sequencing data from pre- and post-treatment leukemia samples. AVAILABILITY AND IMPLEMENTATION: SomVarIUS is written in Python 2.7 and available at http://www.sjdlab.org/resources/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.