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Large Vision-Language Models (LVLMs) have received widespread attention in advancing the interpretable self-driving. Existing evaluations of LVLMs primarily focus on the multi-faceted capabilities in natural circumstances, lacking automated and quantifiable assessment for self-driving, let alone the severe road corner cases. In this paper, we propose CODA-LM, the very first benchmark for the automatic evaluation of LVLMs for self-driving corner cases. We adopt a hierarchical data structure to prompt powerful LVLMs to analyze complex driving scenes and generate high-quality pre-annotation for human annotators, and for LVLM evaluation, we show that using the text-only large language models (LLMs) as judges reveals even better alignment with human preferences than the LVLM judges. Moreover, with CODA-LM, we build CODA-VLM, a new driving LVLM surpassing all the open-sourced counterparts on CODA-LM. Our CODA-VLM performs comparably with GPT-4V, even surpassing GPT-4V by +21.42% on the regional perception task. We hope CODA-LM can become the catalyst to promote interpretable self-driving empowered by LVLMs., Comment: Project Page: https://coda-dataset.github.io/coda-lm/

Powered ankle prostheses effectively assist people with lower limb amputation to perform daily activities. High performance prostheses with adjustable compliance and capability to predict and implement amputee's intent are crucial for them to be comparable to or better than a real limb. However, current designs fail to provide simple yet effective compliance of the joint with full potential of modification, and lack accurate gait prediction method in real time. This paper proposes an innovative design of powered ankle prosthesis with serial elastic actuator (SEA), and puts forward a MLP based gait recognition method that can accurately and continuously predict more gait parameters for motion sensing and control. The prosthesis mimics biological joint with similar weight, torque, and power which can assist walking of up to 4 m/s. A new design of planar torsional spring is proposed for the SEA, which has better stiffness, endurance, and potential of modification than current designs. The gait recognition system simultaneously generates locomotive speed, gait phase, ankle angle and angular velocity only utilizing signals of single IMU, holding advantage in continuity, adaptability for speed range, accuracy, and capability of multi-functions., Comment: Submitted to IROS 2024

Plastic and rubber recycling is an effective means of reducing solid waste to the environment and preserving natural resources. A project aimed at developing a new composite material from recycled high density polyethylene (HDPE) and recycled rubber is currently being conducted at Eastern Illinois University. The recycled plastic pellets with recycled rubber particles are extruded into some HDPE/rubber composite strands. The strand can be further cut into pellets that can be used to fabricate other material forms or products. This experiment was inspired by the above-mentioned research activity. In order to measure Durometer hardness of the extruded composite, a specimen with relatively large dimensions was needed. Thus, compression molding was used to form a cylindrical specimen of 1 in. diameter and 1 in. thickness. The initial poor quality of the molded specimen prompted a need to optimize the processing parameters such as temperature, holding time, and pressure. Design of experiment (DOE) was used to obtain optimum combination of the parameters.