"tanker Collisions And Collision Avoidance Systems: Expert Advice From Maritime Law Attorneys"

 "tanker Collisions And Collision Avoidance Systems: Expert Advice From Maritime Law Attorneys" - Without proper collision avoidance systems, your drivers and vehicles are flying blind. In 2019, 118,000 large trucks were involved in crashes resulting in injury—a 5% increase from 2018, according to the National Safety Council. In addition, a total of 5,005 people died in large truck crashes – a whopping 36% increase since 2010. One highly publicized example occurred in Arizona when a milk tanker traveling at high speeds collided with seven passenger vehicles, killing four people and injuring at least nine. The National Transportation Safety Board (NTSB) will examine whether equipping the truck with electronic safety devices could have prevented the crash. Clearly, truck accidents pose a serious threat to our nation's drivers. If you are committed to improving your fleet safety program, implementing a state-of-the-art collision avoidance system is essential.

A collision avoidance system is a safety system that prevents accidents by helping drivers in the moments before an incident. These systems use many different technologies, including cameras, GPS, G-force sensors, radar, laser and artificial intelligence. They monitor what is happening around the vehicle and in the truck itself. Computers then process the information, which prompts the system to take action.

"tanker Collisions And Collision Avoidance Systems: Expert Advice From Maritime Law Attorneys"

Collision avoidance systems respond to potentially dangerous situations in two ways. The first is to alert the driver to the risk by light, sound, vibration or all of these. Vehicles with front and rear cameras provide visual or audio warnings of obstacles. Collision avoidance systems also help the driver. They do this by overriding the driver's actions in the seconds before a potential collision. For example, if a driver is non-responsive, the system could use the brakes to avoid or mitigate an accident.

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With the use of cameras, lasers and radar, collision avoidance systems include the following to either alert or assist the driver:

Advanced Driver Assistance Systems (ADAS) are safety systems that use advanced technologies to assist drivers. Using a combination of sensor technologies, the system either alerts the driver or takes action with functions such as:

While there is value in implementing collision avoidance systems, other technologies can help take your fleet safety program even further. That's why AI video cams are the future of driver safety. AI dash cams are a proactive taxi safety solution that helps you detect incidents and train drivers in real time. When combined with vehicle telematics, they perform many of the duties of collision avoidance systems. Dashboards also provide you with scorecards and trend data to facilitate ongoing training and ensure accuracy in determining accident liability.

Research shows that collision avoidance systems work. A study conducted by the Insurance Institute for Highway Safety found that automatic emergency braking and pre-collision warnings could prevent more than 40% of crashes in which semi-trucks rear-end other vehicles. And another study revealed that when rear-end crashes occurred, the systems reduced the damage and injuries. In short, collision avoidance systems can save lives. It's time you included them as part of your cutting-edge fleet safety program

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By Rafael Guardeño Rafael Guardeño Scilit Preprints.org Google Scholar 1, * , Manuel J. López Manuel J. López Scilit Preprints.org Google Scholar 1, Jesús Sánchez Jesús Sánchez Scilit Preprints.org Google Scholar 2 and Agustín Consegliere Agustín Consegliere Scilit Preprints.org Google Scholar 1

Received: 2 April 2020 / Revised: 21 April 2020 / Accepted: 22 April 2020 / Published: 25 April 2020

This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles that can be applied to different types of USV. To help in the selection of the SOA method for a particular vessel and to accelerate the pre-tuning process necessary for its implementation, this article proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modeling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods which, due to its low computing load, is used by evolutionary algorithms for the autotuning. To analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. In addition, a mathematical model is proposed and evaluated for use as a USV in the simulation environment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions.

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Unmanned surface vehicles; autonomous navigation; automatic configuration environment; avoiding obstacles; LIDAR sensor modeling; ship modelling; course control; speed control

Currently, the development of the USV is an active and growing field of research. The reason is that the applications of USV are very wide, covering from environmental control, as well as many scientific and commercial applications, to national security and surveillance issues [1]. The required skills

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