Driver Assistance System for Trams: Smart Tram in Smart Cities

Palma, Catello Di; Galdi, Vincenzo; Calderaro, Vito

doi:10.1109/eeeic/icpseurope49358.2020.9160780

Cited by 7 publications

(9 citation statements)

References 21 publications

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“…The availability of automation technologies for automobiles and other road vehicles is increasing, and further advances are expected in the coming years [19]. More and more cars in the cities today are equipped with autonomous driving systems that are aimed at improving road safety and reducing accidents, to halve accident fatalities as quickly as possible [20]. France published a decree that adapts the provisions of the highway code and transport code to allow for automated driving systems and vehicles that are equipped with delegated driving systems on predefined routes (from September 2022).…”

Section: Introductionmentioning

confidence: 99%

Towards ‘Vision-Zero’ in Road Traffic Fatalities: The Need for Reasonable Degrees of Automation to Complement Human Efforts in Driving Operation

Mofolasayo

2024

Systems

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Human factors play a huge role in road traffic safety. Research has found that a huge proportion of traffic crashes occur due to some form of human error. Improving road user behavior has been the major strategy that has been emphasized for improving road traffic safety. Meanwhile, despite the training efforts, and testing for drivers, the global status of road traffic safety is alarming. This research highlights the seriousness of human factors on road traffic safety and provides actionable strategies to greatly reduce the negative impact of human factors on road traffic safety. Motor vehicle safety data that were made available online by the U.S. Bureau of Transportation Statistics were reviewed to evaluate the severity of traffic collisions. To evaluate the extent of human factors in motor vehicle traffic fatalities, data for Canadian motor vehicle traffic collision statistics were reviewed. The study confirms that human factors (such as driver distraction, fatigue, driving under the influence of drugs and alcohol etc.) play a huge role in road traffic fatalities. The need for a reasonable degree of automation to help reduce the impacts of human factors on road safety and recommendations aimed at providing widespread support for a reasonable degree of automation systems in driving tasks are presented. Actionable strategies that can be implemented by policymakers to reduce global road traffic fatalities are also presented.

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Section: Introductionmentioning

confidence: 99%

Towards ‘Vision-Zero’ in Road Traffic Fatalities: The Need for Reasonable Degrees of Automation to Complement Human Efforts in Driving Operation

Mofolasayo

2024

Systems

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“…Various types of Advanced Driver Assistance Systems (ADAS) have been developed for safe driving of different types of private and public vehicles. In 2014 the Society of Automotive Engineers International (SAE) classified 6 levels of driving automation, from the most basic systems (level 0) to 100% autonomous driving one (level 6) [1]. Several research on the ADAS demonstrate that passive forward collision warning (FCW) and intelligent speed assistance (ISA) systems have been efficacious in reducing the number of pedestrian/vehicle collisions [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…In 2014 the Society of Automotive Engineers International (SAE) classified 6 levels of driving automation, from the most basic systems (level 0) to 100% autonomous driving one (level 6) [1]. Several research on the ADAS demonstrate that passive forward collision warning (FCW) and intelligent speed assistance (ISA) systems have been efficacious in reducing the number of pedestrian/vehicle collisions [1][2][3]. ADAS can be defined as a vehicle-based intelligent safety system which may well increase road safety in terms of crash avoidance, crash severity mitigation and user's protection during post incident phases.…”

Section: Introductionmentioning

confidence: 99%

“…catenary-free systems) are currently experiencing a period of strengthening with a notable expansion in new railway lines [18,19]. In fact, since 2000, about 130 new tramway systems have been constructed in the world and many other existing systems have undergone deep renovations [1]. Nowadays, modern trams enjoy great popularity because they are characterized by low environmental impact, adequate construction and maintenance costs, and high transport capacity.…”

Section: Introductionmentioning

confidence: 99%

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Smart Tramway Systems for Smart Cities: A Deep Learning Application in ADAS Systems

Guerrieri

Parla

2022

Int. J. ITS Res.

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Artificial intelligence and deep learning-based techniques undoubtedly are the future of Advanced Driver-Assistance Systems (ADAS) technologies. In this article is presented a technique for detecting, recognizing and tracking pedestrians, vehicles and cyclists along a tramway infrastructure in a complex urban environment by Computer Vision, Deep Learning approaches and YOLOv3 algorithm. Experiments have been conducted in the tramway Line 2 “Borgonuovo –Notarbartolo” (Palermo, Italy) in correspondence of the tramway segments crossing a roundabout having an external diameter of 24 m. A survey vehicle equipped with a video camera was used in the study. The results of the research show that the proposed method is able to search and detect the position and the speed of road users near and over the rails in front of the tram in a very precise way as demonstrate by the estimated values of the Accuracy, Loss and Precision obtained during the neural networks training process. The implementation of this advanced detection method in ADAS systems may increase the safety of novel autonomous trams and autonomous rapid trams (ARTs).

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“…In a recent review [25], the Frontal Collision Warning, FCW and Autonomous Emergency Braking (AEB) systems were evaluated to be implemented using both LiDAR and long-range RADAR by monitoring time to collision (our methodology uses distance measurement).…”

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confidence: 99%

Experimental Validation of LiDAR Sensors Used in Vehicular Applications by Using a Mobile Platform for Distance and Speed Measurements

Vasile¹,

Tudor²,

Sburlan³

et al. 2021

Sensors

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LiDAR sensors are needed for use in vehicular applications, particularly due to their good behavior in low-light environments, as they represent a possible solution for the safety systems of vehicles that have a long braking distance, such as trams. The testing of long-range LiDAR dynamic responses is very important for vehicle applications because of the presence of difficult operation conditions, such as different weather conditions or fake targets between the sensor and the tracked vehicle. The goal of the authors in this paper was to develop an experimental model for indoor testing, using a scaled vehicle that can measure the distances and the speeds relative to a fixed or a moving obstacle. This model, containing a LiDAR sensor, was developed to operate at variable speeds, at which the software functions were validated by repeated tests. Once the software procedures are validated, they can be applied on the full-scale model. The findings of this research include the validation of the frontal distance and relative speed measurement methodology, in addition to the validation of the independence of the measurements to the color of the obstacle and to the ambient light.

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Driver Assistance System for Trams: Smart Tram in Smart Cities

Cited by 7 publications

References 21 publications

Towards ‘Vision-Zero’ in Road Traffic Fatalities: The Need for Reasonable Degrees of Automation to Complement Human Efforts in Driving Operation

Towards ‘Vision-Zero’ in Road Traffic Fatalities: The Need for Reasonable Degrees of Automation to Complement Human Efforts in Driving Operation

Smart Tramway Systems for Smart Cities: A Deep Learning Application in ADAS Systems

Experimental Validation of LiDAR Sensors Used in Vehicular Applications by Using a Mobile Platform for Distance and Speed Measurements

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