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Smart Transportation: A New 2023 Category for The Edison Awards

By: Sandhiprakash (Sandhi) Bhide,
Innovative Business Leader, Technologist, and Strategist; Member of Edison Steering Committee and Expert Judge; President Anwaya Consulting LLC, CEO Balak Drishti; Cofounder/BOD/Investor: FleetNurse Inc.


Smart transportation as the name suggests is about putting smartness in transportation but embedded in the heart or in this case the brain of the vehicle. The vehicle could be a train, a bus, a car, an airplane, a hovercraft, a ship, a boat, a submarine or anything else but normally a motorized vehicle either gasoline powered, or hydrogen powered, or electric or using some other source of energy. Smartness is derived at various degrees of embedded intelligence with a single objective: to take you (or goods) from one place to other so the integrity and safety of the vehicle itself including the assets inside are preserved, to continuously monitor the surroundings to ensure impending threats are recognized and acted upon before they become actual threats, to make sure the vehicles does not create harm to other humans and assets, to learn from ongoing encounters so future impositions are dealt with early and fairly and to monitor and predict things before they become a problem. Smart transportation is a spectrum and not a one thing that makes Smart Transportation.

Smart transportation is not about new fuels, or something specifically about electric or hydrogen based cars, or new exotic materials used in transportation, or about attractive dash board or new method of transportation like Air Taxi. It is also not about what is in the environment like a beacon[i], or GPS, or a recommendation program that tells you which is the best way to go from place A to place B. It is all about what is inside the vehicle and of course, vehicle may depend on outside information like location to act with its intelligence. The vehicle may also use various protocols to connect with other cars, people, obstacles, and surrounding assets, doing sensing, processing, and communicating. All these enable Smart Transportation but they are not Smart Transportation.

The Society of Automotive Engineers (SAE) defines 6 levels of driving automation ranging from 0 (fully manual) to 5 (fully autonomous). These levels have been adopted by the U.S. Department of Transportation. In the first three levels (0-3), the human monitors the driving environment while Level 3-5, an automated system monitors the driving environment. The following is a high level description of these levels.

  1. No Automation: Everything at this level relates to manual control. A human performs all the function such as steering, acceleration, braking, turning heater/music on or off, opening and closing the doors. Here, the vehicle is at the mercy of driver’s instruction (Note: In some cases, I do not know some backseat driving does occur, but we are not talking about at here). Most vehicles on the road today fit this category.
  2. Driver Assistance: Here the driver gets some assistance through a technical mechanism called feedback control. The vehicle feature a single automated system, e.g. cruise control allows you to maintain a constant speed or temperature control or air circulation based on heater setting for driver and the passenger. The vehicle senses the up or down slope and the accelerator is automatically adjusted. Almost all the functions in the car fall in this category.
  3. Partial Automation: For example, in an Advanced Driver Assistance System (ADAS), the vehicle can perform steering and acceleration. This is where both the human and the machine are both engaged and human still monitors all the tasks and take over or override control at any time. Here the automation falls short of self-driving because a human sits in the driver’s seat and can take control of the car at any time. Tesla Autopilot and Cadillac Super Cruise systems both qualify as Level 2.
  4. Conditional Automation: Here the system has environmental detection capability and can make informed decisions for themselves, such as accelerating past a slow-moving vehicle. This is where the vehicle can perform most driving task but human override is still required and so the driver has to be alert. For example, Audi’s A8 featured Traffic Jam Pilot, which combines a Lidar scanner with advanced sensor fusion and processing power (plus built-in redundancies should a component fail) is an example of Conditional Driving Automation. For example, if the car in the front comes too close, the speed of the vehicle is automatically reduce to maintain car to car distance.
  5. High Automation: Here the vehicle performs all the driving tasks under specific circumstance and Level 4 vehicles can intervene if things go wrong or there is a system failure. Here the vehicle can make decisions in most circumstance. Geo-fencing is required and human override is still an option. For example, Canadian automotive supplier Magna’s MAX4 technology (MAX4) can enable Level 4 capabilities in both urban and highway environments. They are working with Lyft to supply high-tech kits that turn vehicles into self-driving cars. These vehicles are geared towards ride sharing. In this particular, in some cases, the driver may be distracted and the vehicle can still take the appropriate decision.
  6. Full Automation: The vehicle performs all the driving tasks under ALL conditions. Zero human attention or interaction is required. The dynamic driving task is eliminated. They will be free from geo-fencing, able to go anywhere and do anything that an experienced human driver can do. For many people, this might be too scary. Just look at the amount of information the system has to process in real time (see note below), which also requires tremendous amount of sensing (using a range of sensors), data processing (using processors) and decision making (using AI chips), fast communication (e.g. using 5G), and highest level of security. When it comes to a threat to life, it becomes scary.


For further reading:

  1. The 6 Levels of Vehicle Autonomy Explained


[1] (For those nerdy out there who want to know some technical details about the IEEE work: Reliable and scalable wireless transmissions for Vehicle-to-Everything (V2X) are technically challenging. Each vehicle, from driver-assisted to an automated one, will generate a flood of information, up to thousands of times that of a person. Vehicle density may change drastically over time and location. Emergency messages and real-time cooperative control messages have stringent delay constraints, while infotainment applications may tolerate a certain degree of latency. On a congested road, vehicles need to exchange information badly, only to find that services are not available due to scarcity of wireless spectrum. Considering the service requirements of heterogeneous V2X applications, service guarantee relies on an in-depth understanding of network performance and innovations in wireless resource management. In this talk, we compare the performance of vehicle-to-vehicle (V2V) beacon broadcasting using random access-based (IEEE 802.11p) and resource allocation-based (C-V2X) protocols and introduce several enhancement strategies to mitigate packet collisions, recover from transmission errors, and support two-user non-orthogonal transmissions. They can become enabling tools toward reliable and scalable V2X for future intelligent transportation systems.)