LiDAR-Based Contour Estimation of Oncoming Vehicles in Pre-Crash Scenarios

Schneider, Kilian; Lugner, Robert; Brandmeier, Thomas

doi:10.1109/ivs.2019.8813825

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Once done correctly, this raises awareness to a point where resources and equipment may be used more efficiently. In Schneider, Lugner & Brandmeier (2019) , road traffic, utilization, and average densities extensively used vehicle congestion assessments. The majority of this data was gathered from images and videos captured by machine vision software.…”

Section: Related Workmentioning

confidence: 99%

Machine learning based IoT system for secure traffic management and accident detection in smart cities

Balasubramanian

Balaji

Munshi

et al. 2023

PeerJ Computer Science

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In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.

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Section: Related Workmentioning

confidence: 99%

Machine learning based IoT system for secure traffic management and accident detection in smart cities

Balasubramanian

Balaji

Munshi

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…[14] LiDAR, clustering, and classification If two targets occupy neighboring segments, or share the same segment where longitudinal separation is less than 0.8 m, the proposed clustering algorithm can group them into a single cluster, resulting in an unresolved measurement [15] LiDAR, convex hull algorithm Accuracy of contour prediction may be damaged due to higher degree curve and noise-induced change in reflection points [17] Onboard sensors, roadside units, GPS, Bluetooth, Ethernet Specific to EVs Internet connectivity required [19] Heuristic technique, V2V, and V2I…”

Section: Refmentioning

confidence: 99%

“…Using LiDAR, a mechanism for estimating the contour of approaching vehicles in the pre-crash phase was explored [ 15 ]. The data are initially combined, and an environmental model containing the identified items and their properties, such as position and shape, is computed.…”

Section: Related Workmentioning

confidence: 99%

Smart Roads for Autonomous Accident Detection and Warnings

Mateen

Hanif

Khatri

et al. 2022

Sensors

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An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident’s location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.

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