Problem Solving Class at Michigan Law Tackles Mixed Fleet Problems

This fall, the University of Michigan Law School is offering its third Problem Solving Initiative (“PSI”) course concerning connected and automated vehicles. The first class, offered in the Winter 2017 semester, involved a team of fifteen graduate students from law, business, engineering, and public policy who accepted the challenge of coming up with commercial use cases for data generated by connected vehicles using dedicated short-range communication (“DSRC”) technology.

In the Fall of 2017, we offered our second PSI Course in CAV—this one to 23 graduate students. That course focused on the problem of Level 3 autonomy, as defined by the Society of Automotive Engineers (“SAE”). Level 3 autonomy, or conditional automation, is defined as a vehicle driving itself in a defined operational design domain (“ODD”), with a human driver always on standby to take over the vehicle upon short notice when the vehicle exits the ODD. As with the first course, our student teams spent the semester collecting information from industry, governmental, and academic experts and proposing a series of innovative solutions to various obstacles to the deployment of Level 3 systems.

This semester, our PSI course is entitled Connected and Automated Vehicles: Preparing for a Mixed Fleet Future. I will be co-teaching the course with Anuj Pradhan and Bryant Walker Smith. Our focus will be on the multiple potential problems created by unavoidable future interactions between automated vehicles and other road users, such as non-automated, human-driven vehicles, pedestrians, and bicyclists.

Although cars can be programmed to follow rules of the road, at its core, driving and roadway use are social activities. Roadway users rely heavily on social cues, expectations, and understandings to navigate shared transportation infrastructure. For example, although traffic circles are in principle governed by a simple rule of priority to vehicles already in the circle, their actual navigation tends to governed by a complex set of social interactions involving perceptions of the intentions, speed, and aggressivity of other vehicles. Similarly, while most states require bicyclists to obey stop signs and traffic lights, most cyclists do not; prudent drivers should not expect them to.

Can cars be programmed to behave “socially?” Should they be, or is the advent of robotic driving an opportunity to shift norms and expectations toward a greater degree of adherence to roadway rules? Will programming vehicles to be strictly rule compliant make CAVs “roadway wimps,” always giving in to more aggressive roadway users? Would that kill the acceptance of CAVs from a business perspective? Is reform legislation required to permit CAVs to mimic human drivers?

More generally, is the advent of CAVs an opportunity to reshape the way that all roadway users access roadways? For example, could the introduction of automated vehicles be an opportunity to reduce urban speeds? Or to prohibit larger private vehicles from some streets (since people may no longer be dependent only on their individually owned car)? These questions are simply illustrative of the sorts of problems our class may choose to tackle. Working in interdisciplinary groups, our graduate students will attempt to identify and solve the key legal, regulatory, technological, business, and social problems created by the interaction between CAVs and other roadway users.

As always, our class will rely heavily on on the expertise of folks from government, industry, and academia. We welcome any suggestions for topics we should consider or experts who might provide important insights as our students begin their discovery process next week.