The global automotive, transportation, and logistics industry is a lucrative yet demanding economic giant. Not only does it connect many countries across the world, but it is also responsible for getting people and cargo from point A to B at a great cost. Safety of humans and products alike are constantly on the line as delivery timelines are often very tight. When the industry falters, we can expect to lose billions worldwide. Customer trust and safety will also go down and trickle down the supply and demand chain.
Let’s look at one of the smaller scales of this industry. Human error accounts for 94% to 96% of vehicular accidents according to a 2016 study the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA). As a result, the world economy is predicted to lose approximately $1.8 trillion dollars between 2015 to 2030 due to fatal and nonfatal crash injuries.
In addition to injuries and fatalities, those who get held up by roadside accidents are subjected to loss of productive work hours, rising insurance and medical costs, repair bills, and other legal costs. Heavy traffic congestion that comes with pileups also impact environmental pollution. Vehicle owners look to technology in countering the large-scale impact of these accidents. Advanced artificial intelligence (AI) is integrated into vehicles to lessen human errors for greater road safety for all.
Advanced 2D and 3D Computer Vision for Self-Driving Vehicles
Enabling self-driving vehicles with advanced Computer Vision (CV) technology using 2D and 3D (Lidar) data — such as image, audio, video, light detection and ranging, radar, and ultrasonic among others — can be applied to safely transport humans and cargo. When successful, it leads to safer road travel with far fewer accidents resulting in reduced medical, insurance, repair, and policing costs. These technologies can also reduce environmental pollution and increase human productivity.
Machine Learning-Driven Predictive Analytics for Supply Chain Optimization
Supply chain optimization requires businesses to ensure the right products are readily available to meet customer demands. This can be quite difficult for humans to manage. Using multi-variate historical data, machine learning can be driven by statistical models that analyze billions of data point to make accurate demand predictions and suggest optimal supply chain decisions. Smooth operations help businesses reduce product waste, storage, and transportation costs. They can maximize production schedules, increase sales and profits, and improve customer satisfaction.
IoT Sensor Data for Predictive Vehicle Maintenance
IoT (Internet of Things) sensors can be installed to track the health of vehicles and ensure optimum performance and reliability. Predictive diagnosis from machine learning algorithms can save vehicle owners expensive repairs and disruptions in operations. In addition, these algorithms can also proactively recommend maintenance schedules, therefore, leading to fewer vehicle breakdowns, reduced repair and business disruption costs, and increased business efficiency.