Fleets Categories of - drivebuddyAI

Fleet Manager’s Decision Making Capabilities Plays a Major Role in Enhancing your Fleet & Driver’s Safety

Managing fleet safety is a crucial task, an important responsibility as a lot depends upon it. With the number of vehicles increasing every day, the potential risk of road accidents as well as other safety hazards also tends to rise. In view of this, the role of the fleet managers ensuring the safety of their vehicles & drivers at all the times has become and will always be very vital.

With more & more commercial vehicles and passengers falling prey to road accidents these days, it is vital to keep stringent driving and safety practices intact. Apart from the safety of your drivers & fleets, it is equally important to consider the safety of other vehicles and pedestrians on the road. Thus, this is where Fleet Safety Insights come into play.

What are Fleet Safety Insights?

In a nutshell, Fleet Safety Insights refer to the data driven insights that enable the fleet managers to make better decisions based on the actionable inputs they receive to improve and manage their drivers & fleets.

The word ‘data insights’ might sound complex to you but it is the heart of technology as we know it. Fleet managers obtain these insights through various sources like driving behaviour, vehicle sensors, fleet GPS tracking as well as other external sources.

How drivebuddyAI assists Fleet Managers improve fleet & driver safety by using AI

Artificial intelligence (AI) has the potential to revolutionize the way fleet managers make decisions and operate their fleet. By providing real-time data analysis and predictive capabilities, AI can help fleet managers make informed, data-driven decisions that can improve efficiency, reduce costs, and increase overall profitability.

To increase your fleet manager’s capabilities, giving them access to AI-powered technology is imperative. It will surely make their job easily manageable. Here is how drivebuddyAI’s ADAS (Advanced Driver Assistance System) makes the difference.

Advanced Driver Assistance Systems (ADAS) are becoming increasingly reliable because of their results in world of fleet management as a way to improve safety on the roads. drivebuddyAI’s ADAS cameras captures in-cabin videos, roadside videos and surroundings, providing valuable visual information that can help fleet managers make informed decisions about routing, safety, and other important factors. In addition, an ADAS camera also provides data on vehicle speed, braking, and other performance metrics that can help fleet managers optimize their operations.

Four significant ways through which drivebuddyAI ADAS improve your Fleet Manager’s decision making:

Provide Real-Time Alerts & Notifications: Real-time alerts are one of the most effective ways of creating transparency between the fleet managers & the fleet drivers. drivebuddyAI gives fleet managers real-time alerts regarding potential risk to the safety on the road. These alerts include warnings for speeding, hard braking, sudden accelerations, signal breaks, lane departures and other hazardous driving behaviours.
Enhance your Driver’s Performance: driverbuddyAI ADAS deeply analyzes your driver’s driving behaviour and provides core insights on it . This includes data on the driving habits, fuel consumption and other key parameters like drowsiness detection, distraction alerts and phone usage alerts. This also allows the fleet managers to take driver coaching into consideration if required.
Improve Fleet’s Route Planning: drivebuddyAI ADAS cameras collect real-time traffic data and other roadside data to help fleet drivers and managers to improvise their route planning. A major and primary impact of this is reducing fuel consumption, emission and other fleet operational costs. Improving your route planning also helps in collision avoidance and prevents unnecessary risks on the road.
Optimisation of Vehicle Maintenance: Maintaining your fleet is equally important as a well-maintained vehicle ensures more safety. A fleet manager could use data analytics to determine the optimal time to perform maintenance on a vehicle based on its usage history and make data driven decisions for its future usage. This could also help in reducing costly downtime and to improve the overall efficiency of the fleet.

To conclude, drivebuddyAI’s ADAS camera is a powerful tool that can help fleet managers make the right decisions to ensure the safety of their drivers and fleets. drivebuddyAI’s real-time alerts and notifications, enhanced driver performance, improved route planning, and optimized vehicle maintenance capabilities, has helped fleet managers improve their fleet’s operations and reduce the risk of accidents and other safety hazards by making better decisions for the all.

January 17, 2023

Driving Behavior · Fleet Operations · Fleets

Simulation for Training Autonomous Driving Systems

Self-driving or autonomous driving systems are one of the most promising applications of near-term Artificial Intelligence development and research. This path-breaking research utilizes an immense amount of data – collected while driving – that is labelled and contextually rich. The levels of driving autonomy have been defined quite clearly by the NHTSA in the USA:

Stages of Autonomy in Driving

Level 0 – No Automation
The driver is completely in charge.
Level 1 – Function Specific Automation
Single control functions like speed selection, braking or lane following are automated.
Level 2 – Combined Function Automation
More than one control function is automated. The driver is expected to be available to take over the control of the vehicle at all times and at short notice.
Level 3 – Limited Self-driving Automation
The vehicle remains in control most of the time. The driver is expected to be available to occasionally take over the controls with comfortable transition times.
Level 4/5 – Full Self-driving Automation
The vehicle stays in control all the time. The driver is not required to be available to take over the controls at any time.

From the perspective of complex perception and controls, the technology that solves autonomous driving can possibly be extended to other important tasks such as action and motion recognition in videos. An economically attractive approach for autonomous driving that still expands the AI is for it to be a vision-based system – the same sense that humans use when driving. Vision-based control systems and reinforcement learning became practical and mainstream mainly due to technological developments like deep and recurrent neural networks and unbounded access to world or game interactions. These interactions allow for the possibility of revisiting previous states with new policies and simulating possible events for further training of deep neural network-based controllers.

Methods of Testing Autonomous Functions

Virtual Tests/Simulations – These tests encompass the analysis of a large number of scenarios, environments, system configurations and driver characteristics.
Proving Ground Tests – For testing the performance of driving robots and self-driving cars during critical manoeuvres.
Field Tests – For the investigation of real driving situations and calibrating system specifications.

In the case of controls problems in which the unbounded interaction of a learning agent with the open world is not practical, there are two possible ways out. One is to hand code a simulator, and the other is to learn to simulate. Hand coding involves considerable expertise in defining the rules of physics and programming the randomness of the world into the code. Simulation is crucial to efficiently verify the huge amounts of functional requirements!

Latest Developments in the Testing of Autonomous Vehicles

Methods

Greater simulation for verification of control algorithms and traffic rule compliance.
Structured search for functional deficits, instead of waiting for them to arise during test driving

Functions

Continuously assessing and adapting to external conditions and rules
Reliably judging if the limits of vehicle autonomy are close
Announcing the end of the autonomous mode early enough that the driver can take over (Level 3 Autonomy)
Bringing the vehicle to a safe stop (in Level 3 Autonomy) if the driver fails to take over

Major Reasons for Accidents during Autonomous Driving

Failure of components or hardware issues
Deficiency in sensing road, traffic and environmental conditions
Control Algorithm deficiency in complex and difficult situations
Accidents caused by driver behaviour
Faulty interaction between the driver and vehicle

Frequent simulation of such situations is critical to the mitigation of these causes of accidents.

Challenging Traffic Situations Recreated in Simulations

When following: Preceding car drives into a traffic jam without braking
When a car cuts in: The cut-in vehicle breaks hard, leaving no space for evasion
When a car cuts out: Preceding car cuts out just before an obstacle, leaving no room for evasion

A number of virtual models need to be developed in order to simulate these situations.

Virtual Models Required for Driving Simulation Platforms:

Sensor Models: Activity simulation of vehicle sensors like the camera and other sensors Vehicle Models: Vehicle dynamics for lateral and longitudinal vehicle motion simulation Road Model: Road network model for the provision of virtual road and infrastructure information Traffic Model: Traffic simulation for vehicles, pedestrians and bicycles with driver behaviour models and vehicle models. These models need to be original and generate realistic as well as unique situations to train autonomous driving systems.

Conclusion

Before autonomous vehicles can drive you anywhere, they have to prove that they will not drive you into trouble. A huge number of test driving kilometres will be needed to attain this level of reliability and safety. This is not practically and economically possible or sensible, which is why simulations are needed to train these autonomous driving systems.

About drivebuddyAI

drivebuddyAI is a fleet management system with AI technology, designed to provide visibility and help with actionable insights to improve operations – from driving safety to driver selection & driver coaching. drivebuddyAI’s technology brings with it a driver drowsiness detection system, driver fatigue detection, and a collision warning system for increased driving safety. drivebuddyAI’s autonomous technology has also been tested on the simulators discussed in this article, in addition to proving-ground and on-road field tests. Visit drivebuddyai.co for further information!

July 29, 2022

Driving Behavior · Driving Safety · Fleet Operations · Fleet Safety · Fleets · Productivity · Telematics

How drivebuddyAI Delivers On-Road Safety for Fleets Delivering Critical Loads of Chemicals and Gas

A deep dive into the key technology-led factors that are enabling safe transportation of high-value, critical loads.

For trucking companies, the cargo is the most valued asset of their business. It is crucial to move them safely and securely, often over long distances.

However, many factors affect safety on the road during the long haul
The challenge becomes critical when transporting chemicals and gases. An accident can prove to be catastrophic.

The transportation of bulk chemicals is a high-risk, high-value business. The long-haul nature of the shipments and the large number of kilometers traveled make these operations susceptible to accidents that can cause significant damage to property and loss of life.

Major pain points for fleets

When loaded, a truck with 40,000 liters of chemicals is valued at approximately 3 to 4 crore rupees. To maintain safe transportation of these high-value, critical loads – specially trained drivers for driving longer routes are needed. They need to be agile, alert and cognizant of their surroundings, more than usual. But, finding such a well-trained driving resource pool is often difficult.

Moreover, there can be varying road conditions that arise due to extreme weather or poor road quality that can cause delays and increase the turnaround time (TAT) of critical shipments.

This shortage of drivers only results in mounting pressure on the already existing drivers as they have to drive for longer periods, and more often. And there is also the question of safety of the drivers themselves arising from distraction and drowsiness during long-haul drives, especially at night.

In addition to following all local regulations and driving at safe speeds, drivers must also be able to identify potential hazards along their route before they occur so they can take appropriate precautions.

How does drivebuddyAI keep your fleet safe?

drivebuddyAI’s Intelligent Driver and Fleet Safety Platform offers a suite of features to help drivers comply with driving standards. With real-time alerts, continuous monitoring and coaching have improved driver performance over the period of time.

*Data observed for a chemical tanker driver between November ’21 – April ‘22
Source: drivebuddyAI Analytics

“ I have been using drivebuddyAI’s intelligent ADAMATICS system for over six months now, and my experience has been fantastic. I drive approximately 300 to 400 kilometers daily. The system alerts me if there is another vehicle in close proximity and reminds me to maintain a safe distance. It also helps me stay attentive by detecting my drowsiness and fatigue, which enhances my overall driving performance. With drivebuddyAI, I feel my driving has improved over the period of time. I suggest other drivers use it too.”
– Chaman Singh, Fleet Driver

Our latest data shows that the driver assistant device started improving drivers performance within two months of deploying the technology. And in less than six months, the driver safety score had jumped up by almost 5% to reach 91.92%.

*The final scores represent the driver’s performance improvement after each distraction was addressed with relevant training
Source: drivebuddyAI Analytics

With AI-powered telematics platform, critical loads and long-haul journeys across different conditions become safer and enable fleets to operate at their maximum potential. Some of the advantages of an intelligent driving assistance system include:

Driving assistance
In-vehicle realtime alerts to protect against collisions & distractions.

Critical event notifications
Triggered by events like speeding, harsh braking, unsafe lane changes to help managers monitor fleets on cloud/phone.

Driver scoring and evaluation
Event generation based feature to evaluate drivers over time.

Driver coaching
Personalized coaching based on vehicle data incentivizes safe, better, optimized driving.

Driving behavior analysis
Deep insights into driving habits to help keep fleets safe and efficient.

Driver recognition & authorization
For large or small fleets to identify and help prevent unauthorized access to fleets.

With drivebuddyAI’s AI-powered telematics platform, fleet managers can analyze the right data at the right time, identify irregularities far ahead of human abilities and help fleets operate at their maximum potential.

June 7, 2022

Fleets