Browse Topic: Environment
This paper presents the design and implementation of a Semi-Autonomous Light Commercial Vehicle (LCV) capable of following a person while performing obstacle avoidance in urban and controlled environments. The LCV leverages its onboard 360-degree view camera, RTK-GNSS, Ultrasonic sensors, and algorithms to independently navigate the environment, avoiding obstacles and maintaining a safe distance from the person it is following. The path planning algorithm described here generates a secondary lateral path originating from the primary driving path to navigate around static obstacles. A Behavior Planner is utilized to decide when to generate the path and avoid obstacles. The primary objective is to ensure safe navigation in environments where static obstacles are prevalent. The LCV's path tracking is achieved using a combination of Pure Pursuit and Proportional-Integral (PI) controllers. The Pure Pursuit controller is utilized as lateral control to follow the generated path, ensuring
Accidents during lane changes are increasingly becoming a problem due to various human based and environment-based factors. Reckless driving, fatigue, bad weather are just some of these factors. This research introduces an innovative algorithm for estimating crash risk during lane changes, including the Extended Lane Change Risk Index (ELCRI). Unlike existing studies and algorithms that mainly address rear-end collisions, this algorithm incorporates exposure time risk and anticipated crash severity risk using fault tree analysis (FTA). The risks are merged to find the ELCRI and used in real time applications for lane change assist to predict if lane change is safe or not. The algorithm defines zones of interest within the current and target lanes, monitored by sensors attached to the vehicle. These sensors dynamically detect relevant objects based on their trajectories, continuously and dynamically calculating the ELCRI to assess collision risk during lane changes. Additionally
The legislation of CEV Stage V emission norms has necessitated advanced Diesel Particulate Filter calibration strategies to ensure optimal performance across diverse construction equipment applications in the Indian market. Considering the various duty cycles of cranes, backhoe loaders, forklifts, compactors, graders, and other equipment, different load conditions and operational environments require a comprehensive strategy to enhance DPF efficiency, minimize regeneration frequency, and maintain compliance with emission standards. The DPF, as an after-treatment system in the exhaust layout, is essential for meeting emission standards, as it effectively traps particulate matter. Regeneration occurs periodically to burn the soot particles trapped inside the DPF through ECU management. Therefore, understanding soot loading and in-brick DPF temperature behavior across various applications is key. This paper explores the challenges in DPF calibration for CEV Stage V and provides a
This research paper offers a comprehensive evaluation of lithium-ion battery recycling methods, tracing the entire journey from global demand to the practical challenges and solutions for sustainable battery recycling. It starts with the analysis of worldwide LIB demand growth alongside the exponential growth in volumes of spent batteries and recycling rates. The study focuses on the imbalance in production and recovery of critical battery components and its environmental and economic effects. The paper then systematically examines six major recycling methodologies: mechanical, pyrometallurgical, hydrometallurgical, biotechnological, direct, and ion-exchange recycling. It goes into detail about their advantages, limitations, and roles in maximizing the recovery of valuable metals such as lithium, cobalt, and nickel. Traditional techniques like hydrometallurgical and pyrometallurgical methods, and emerging approaches including bioleaching and ion-exchange, are evaluated for their
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