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Military ground vehicles operate in off-road environments traversing different terrains under various environmental conditions. There has been an increasing interest towards autonomous off-road vehicle navigation, leading to the needs of terrain traversability assessment through sensing. These methods utilized data-driven approaches on classical robotic perception sensing modalities (RGB cameras, Lidar, and depth cameras) positioned in front of ground vehicles in order to observe approaching terrain. Classical robotic sensing modalities, though effective for describing environment geometry and object detection and tracking, aren’t able to directly observe features related to compaction and moisture content which have significant effects on the moduli properties governing terrain mechanics. These methods then become very specialized to specific regions and environmental conditions which are inevitably subject to change.

Among known electrochemical batteries, Lithium-ion batteries are best suited for portable electronics and electric vehicles because of their highest gravimetric and volumetric energy density. The anode materials used so far in commercial lithium-ion batteries are still graphite, but its specific capacity has been unable to meet the market demand. SnO2 is a promising alternative due to its high specific capacity with 782 mAh/g, but there are many bottlenecks when it is used as anode material solely, such as poor electrical conductivity, high volume change rate. In order to suppress these deficiencies, porous nano-sized SnO2/graphene/activated carbon (CAC/GN/SnO2) composites with high electrochemical performance are prepared via hydrothermal method followed by a facile calcination.

Driving inattention caused by drowsiness has been a significant reason for vehicle crash accidents, and there is a critical need to augment driving safety by monitoring driver drowsiness behaviors. For real-time drowsy driving awareness, we propose a vision-based driver drowsiness monitoring system (DDMS) for driver drowsiness behavior recognition and analysis. First, an infrared camera is deployed in-vehicle to capture the driver’s facial and head information in naturalistic driving scenarios, in which the driver may or may not wear glasses or sunglasses. Second, we propose and design a multi-modal features representation approach based on facial landmarks, and head pose which is retrieved in a convolutional neural network (CNN) regression model. Finally, an extreme learning machine (ELM) model is proposed to fuse the facial landmark, recognition model and pose orientation for drowsiness detection. The DDMS gives promptly warning to the driver once a drowsiness event is detected.