Intelligent Computer Vision Enabled DriveCam | Computer Vision Startup Ideas

Sourav Paul

This Artifical Intelligence based Startup idea from YoStartups is to develop an Intelligent Computer Vision enabled DriveCam. The Startup would develop a Drivecam with Computer Vision and Deep Learning system that will act as a collision avoidance system and an aid for stress free driving. It will use the windscreen glass to communicate and display critical insights of the road and neraby environment.

A set of high definition cameras fitted in the vehicle captures road information including the position of other vehicles, traffic signals, roadblocks and pedestrians. The video is analysed real time by a deep learning processor that displays the information on the smart windscreen glass.

Market Definition For Intelligent Computer Vision enabled DriveCam

As per a research report by Smart Cities World, traffic congestion has risen by 23% since 2008. City dwellers spend close to 100 hours a year, over the optimum travel time, navigating traffic jams. Further, every vehicle/ driver lose nearly $1000 while waiting in traffic.

Apart from the economic and time impact, congestion leads to health-damaging air pollution and high levels of stress that impacts the overall life and productivity of all commuters. While advancements in public transportation and ride sharing are common solutions, the human-driven road transport system is indispensable because it provides connectivity to the last mile/ doorstep.

Competitor Analysis  For Intelligent Computer Vision enabled DriveCam

NetraDyne is a company that provides IoT solution using the advancement in Deep Learning from a video, visual and sensory inputs. They target industries where timeliness of actionable data is a crucial differentiator of performance.

TwentyBN is a computer vision company that builds advanced machine learning systems that understand the video. They provide solutions for industries like smart home or automotive to enhance the performance of video applications for activity recognition and human-machine interaction.

Pain Point and Target Audience For Intelligent Computer Vision enabled DriveCam

The target end customers for this business are the city based drivers. Road conditions vary every minute and drivers spend a significant portion of the driving time trying to analyse the road conditions. The same data may be interpreted differently by different drivers, thus leading every driver to take un-coordinated actions in a congestion scenario. This leads to further congestions. Traffic lights in most large cities are not smart, and the number of traffic police force is not sufficient to man every section of the road network.

There is, therefore, a need for vehicles/ drivers to be able to talk to each other in a congestion zone. This communication may either be a one-to-one dialogue or it a system that gives a set of coordinated instruction to every driver. An an Intelligent Computer Vision enabled DriveCam using the power of IoT can easily fill this much nodded market gap for car drivers.

Value Proposition For Intelligent Computer Vision enabled DriveCam

The value proposition is to develop a device using computer vision technology and use deep learning to display critical insights of the road and traffic environment on the smart glass windscreen. City traffic is on the increase, and road conditions change very frequently depending on the time of the day, maintenance on roads, and environment conditions such as rain. The drivers thus need real-time road information for safe and stress-free driving. This Intelligent Computer Vision enabled DriveCam, will also act as a collision avoidance system and make driving a safer activity.

Using the feed from high definition computer vision camera, Information such as the safe distance from other vehicles, the presence of obstructions/ blocks, the data from road signals, pedestrians trying to cross the road, vehicles trying to switch lanes, if shared with the drivers can not only prevent accidents but also allows the driver to focus on taking quick actions. The driver is thus not bogged down trying to analyse the road conditions. Driving thus becomes stress-free and increases the overall productivity of the individual.

Business Model For Intelligent Computer Vision enabled DriveCam

There are two different customer types for this business – Future vehicle owners and Current vehicle owners.

To target Future vehicle owners, the company should initially tie up with auto manufacturers to include the above technology in specific models. Over time, an Ingredient Branding model needs to be adopted, highlighting safety and reduced stress as values for the customer. A good way to target existing vehicle owners would be to target fleet owners and ride hailers. Again, the safety of travel can be a key feature to market the concept to the end user.

Way to market For Intelligent Computer Vision enabled DriveCam

The firm will have to develop models that can be integrated with all types of vehicles. The way to promote this concept is by business-to-business marketing. The startup should focus at first on a few vehicle models, demonstrating its benefits and then expand to other models. The data collected from the systems will help in further learning and also support the firm to modify their systems.

Milestones For Intelligent Computer Vision enabled DriveCam

The startup should focus on developing the video recorders, train the system with deep learning, develop the smart glass windscreen, conduct trials and then launch a basic version. Cars are an ideal option to start with, before moving on to other vehicles such as buses and trucks. By the end of a year, the startup should target to expand to all car manufacturers in a country.

Investment Needed for Prototype For Intelligent Computer Vision enabled DriveCam

 An an entrepreneur, 200-250 thousand from angel investors and incubators, for developing and testing the concept. Angel investors or incubators such as Startup Autobahn, Lab 1886, Techstars Mobility, DRIVE Automotive Technology Incubator, TAMO provide both the seed money and domain-specific guidance.

Team Capability For Intelligent Computer Vision enabled DriveCam

The core team would be comprised of AI and IoT engineers. It would be good to have automotive engineers with strong coding and analytical background, in the team.

Investors/ Expert Take For Intelligent Computer Vision enabled DriveCam

Gartner predicts that by 2020, the key performance indices in any urban policy will include Climate Change, Resilience and Sustainability. Urban mobility will drive sustainability targets, and artificial intelligence and IoT-enabled solutions will be the key to drive congestion issues. Traffic congestion so far has been addressed in silos without a broader view of the impacts in even the near locality. The next level of solutions to address the rising traffic issues will have to be therefore based on information and data sharing to arrive at a holistic and optimised operation.

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