In May 2017, Worcester Polytechnic Institute (WPI) held its second annual Connected and Autonomous Vehicles Summer School speaker series, sponsored by the Institute of Electrical and Electronics Engineers Vehicular Technology Society (IEEE VTS). The event included two days of lectures and discussions.
Danjue Chen, Professor at UMass-Lowell and UMTC Affiliate, discussed the impacts of connected and automated vehicles (CAVs) on traffic operations and highway traffic flow, and how CAVs can help optimize roadway capacity and traffic control. Professor Chen is the featured researcher in this month’s Innovative Outlook (IO).
Hossein Pishro-Nik, Professor at UMass-Amherst and UMTC Affiliate, spoke about Vehicular Ad Hoc Networks (VANETs) for vehicle-to-vehicle and vehicle-roadway infrastructure communications. His talk discussed the relationship between communications and safety in VANETs, how VANETs can be customized for different traffic conditions and individual drivers, and the issues of privacy in VANETs and Internet-connected devices and applications. Professor Pishro-Nik’s research is described in more detail in another post.
Jason Rife, Professor at Tufts University, presented information on different GPS-based technologies and applications that can assist with automated vehicles and navigation, even in dense urban areas with limited sky visibility.
Bob Sletten, Engineering Manager at Autoliv, a company that develops automotive safety systems for auto manufacturers, spoke about radar technology in automotive applications.
Akshay Rajhans, Senior Research Scientist at MathWorks, spoke about model-based design for connected autonomous vehicles. As described in the WPI conference program, “model-based design makes use of computational models of systems under design that are developed, optimized and checked after correctness specifications throughout the design cycle.”
Alexander Wyglinski, WPI Professor and organizer of the conference, provided an overview of vehicular communication systems and the fundamental concepts for understanding, designing, and implementing them.
The keynote speaker at the gathering was Katherine Fichter, Assistant Secretary for Policy Coordination at MassDOT. Ms. Fichter discussed the potential future impacts of driverless vehicles under different scenarios, including a Driverless Utopia and a Driverless Nightmare that were described in Driving Towards Driverless Cars, a blog by Lauren Isaac. Under these scenarios, autonomous vehicles are expected to improve roadway safety, increase vehicle miles traveled, and reduce greenhouse gas emissions, but there are other potential impacts that are less certain. For example, will more driverless cars reduce urban sprawl or increase it, and how will the mobility of low-income people be impacted? As Ms. Fichter discussed, there are questions as well about how autonomous vehicles will be regulated and insured. One big challenge is that current regulations are all based on the idea that vehicles have human operators; this will need to change.
In the future, intelligent transportation systems (ITSs) will involve connected vehicles, including driver-assisted vehicles and self-driving cars, as well as on-board mobile devices, sensors, and the software and algorithms that govern the functioning of these devices and their communications. Despite recent improvements, each year tens of thousands of lives are lost and billions of dollars are wasted because of traffic inefficiencies in the United States alone. Improvements in the transportation systems could have an enormous impact on lowering these statistics.
In this research, we aim to establish a new approach in design of safety systems, which is based on the individualization and customization of these systems to specific drivers and their environments. This means that wireless communication protocols, as well as algorithms that communicate to users, can be designed in an intelligent way in order to take advantage of all the statistical data that is available regarding the driver and his/her environment.
To accomplish this objective, we can use the technology to collect driver performance data and subsequently learn driver characteristics and driving strategies. This information, along with data collected from other vehicles and roadside units, can be used to customize the technology to each driver. With this, it is possible to adapt warnings or automatic control strategies to each driver. Meanwhile, vehicle-to-vehicle (V2V) communication can be dynamically tuned to make efficient use of finite bandwidth and guarantee the transmission of information critical to safety.
In this way, we should consider that there is an uncertainty of the message delivery between two specific vehicles, while other vehicles might also transmit simultaneously. Our research shows that by proper adaptation of wireless communication and warning algorithms, we can potentially reduce accident fatalities by a considerable amount.
To understand the benefit of V2V communication, consider a traffic stream where a chain of vehicles moves with same speed. When the first vehicle in the chain brakes, the driver of the following vehicle applies the brake after her perception reaction time (PRT). If no intervehicle communications are employed, vehicle Vi applies the brake after the sum of PRTs up to the driver i. With the communications, this time will change to the communications delay plus PRTs of the driver i. This is shown in Figure 1.
Some drivers may think that some of the received warning messages are not needed, because the drivers are aware of their own response time empirically and they know that they can react to stimuli fast. These warning messages are false alarms for these drivers. These warning messages may frustrate the drivers with an overly high number of false alarms, causing them to ignore warnings or even disable the system. To address this issue, we propose estimating the PRT of drivers and personalizing warning messages based on individual PRTs. Figure 2 shows that at the same accident probability for each driver, the false alarm rate can be reduced by at least 30% by employing the estimated individual distribution instead of the population distribution. Thus, it is of vital importance to minimize false alarms so that the system sends warnings only when they are needed.
Now, we should determine how channel access probabilities of vehicles and vehicular communications can be adapted to drivers’ characteristics. In a network of vehicles, each vehicle transmits with a specific probability in the transmission medium. Large channel access probabilities lead the system to excessive interferences and, consequently, low probability of packets being successfully received (success probability), while very small values reduce the success probabilities since the probability of the favorite transmission is low itself. Therefore, there is an optimal value, given both the physical data obtained by vehicular networks and the communications protocol requirements, which results in lower collision probability of vehicles. We can find the expression of packet success probability in a network of vehicles based on channel access probability of vehicle.
We then use a recursive algorithm to tune the transmission probability of each vehicle based on the individual characteristics of drivers. The PRT of the driver, traffic conditions, and communications delay are three factors that play roles in assigning channel access probabilities to vehicles. In simple terms, we categorize the drivers into safe and unsafe drivers based on perception-reaction time. The unsafe vehicles are the ones whose drivers have long perception-reaction time and low distance to the vehicle in front. In other words, unsafe vehicles have higher collision probability. Then we assign different channel access probabilities to unsafe and safe vehicles respectively.
Figure 3(a) illustrates the collision probabilities when channel access probabilities are assumed to be equal for all vehicles. Figure 3(b) shows the scenario in which different channel access probabilities are assigned to unsafe and safe vehicles. The minimum collision probability in the second scenario improves by 25%.
Our simulation results confirm that unsafe vehicles need to inform other vehicles of their perilous situation more frequently than do safer vehicles. In other words, with higher channel access probability for unsafe vehicles, we can achieve lower collision probabilities.
Written by Hossein Pishro-Nik, UMTC Research Affiliate and Associate Professor in the Department of Electrical and Computer Engineering (ECE) at UMass-Amherst. This research was supported by the National Science Foundation under Grant CCF– 0844725 (PI: Hossein Pishro-Nik). It is a joint work with ECE PhD students Mohammad Nekoui, Ali Rakhshan, and Mohammad Kohsravi, and Professor Daiheng Ni from the UMass-Amherst Department of Civil and Environmental Engineering. For more information and access to published papers, please visit http://www.ecs.umass.edu/ece/pishro/publications.html.
General Motors Company (GM) announced in mid-June that it completed production of 130 self-driving Chevrolet Bolt electric vehicles for testing automated vehicle (AV) technologies on-road. These highly automated vehicles (HAVs) join GM’s more than 50 Chevrolet Bolts with AV technologies already operating on public roads in San Francisco, Detroit, and Scottsdale, Arizona. In April 2017, Spectrum, the flagship magazine for the Institute of Electrical and Electronics Engineers (IEEE), reported on GM plans to have as many as 300 more self-driving vehicles on-road, presumably including the recently completed 130 vehicles. According to Spectrum, GM would then have the largest HAV fleet on-road not only in the United States, but worldwide. Google-based Waymo has the second-largest AV fleet in the United States, with an estimated 160 vehicles on-road.
In GM’s announcement regarding the 130 new self-driving Bolts, GM Chairman and CEO Mary Barra is quoted: “This production milestone brings us one step closer to making our vision of personal mobility a reality …. Expansion of our real-world test fleet will help ensure that our self-driving vehicles meet the same strict standards for safety and quality that we build into all of our vehicles.” CEO Barra has also said that “no other company today has the unique and necessary combination of technology, engineering and manufacturing ability to build autonomous vehicles at scale.”
The new self-driving version of the Chevrolet Bolt is the second generation of GM’s AVs and is capable of handling almost any roadway situation without human driver intervention. The new Bolts are equipped with the latest technologies in cameras, radar (LiDAR), sensors, and related hardware. “There are even a couple of cameras that are dedicated just to seeing traffic lights to make sure you don’t run red lights,” said Kyle Vogt, CEO of Cruise Automation, a self-driving software company that GM acquired in 2016. The GM HAVs always have an employee in the driver’s seat for safety reasons, just in case any intervention is needed. Almost all states with HAV regulations also have the requirement that a human operator be present.