Virginia Tech Transportation Institute discusses key issues in the deployment of automated trucks
Automotive IQ sat down with Johan Engstrom, Group Leader, Human Factors and Advanced Systems Testing at Virginia Tech Transportation Institute, and talked about key issues in the deployment of automated trucks, ahead of the conference where Mr. Engstrom also will be presenting.
What are the major technical issues in creating multi-brand platoons and how do we overcome it?
Standardization of vehicle-to-vehicle communications such as specific message sets for platooning (as an extension to SAE J2735) are of course important to facilitate interoperability. However, I believe that the key challenge will be to go from single- to multi-fleet platoons (rather than to multi-brand). Of key importance will be services for forming and managing platoons, possibly integrated as part of fleet management systems. Key functions of such services include means for finding nearby and trustable vehicles to platoon with (potentially from competing carriers) and means for fair sharing of the benefits between the leader and the follower(s) if they represent different companies (e.g., dealing with the fact that the fuel savings are typically higher for the follower).
How can we accommodate trucks with different engine and brake capacities, emission properties etc.?
Trucks in the platoon will need to share information on their relevant properties such as vehicle type, size, mass, engine and braking capacity. It may be foreseen that platoons will primarily be formed by vehicles with roughly similar characteristics. The vehicle properties are also important for how the platoon is formed. For example, in platooning pairs, the vehicle with the highest braking capacity should be assigned the role as follower.
What are the software challenges for developing increasing levels of automation and how algorithms and software can/will adapt for future technical platoon requirements?
My knowledge in software engineering is rather limited so I’m unable to offer a detailed answer. In general, external sensing and V2V communication reliability, redundancy and cybersecurity are issues that become critical with increasing levels of automation.
What infrastructure advancements need to be done to make cooperative, connected and automated driving a reality?
Current near-market automated driving systems for trucks, such as Driver Assisted Truck Platooning (DATP), do not rely on specific infrastructure developments. However, future automated driving systems will likely benefit strongly from advancements in vehicle-to-infrastructure communication technologies. Moreover, it seems likely that certain roads will be adapted and/or optimized to accommodate more advanced forms of automated vehicles (e.g., longer platoons, higher levels of automation). If feasible, creating a safe separation between automated heavy vehicles and other traffic (e.g., by means of dedicated and/or protected lanes), would remove some of the key hurdles for more near-term deployment of highly automated trucks.
What are the challenges and benefits in the area of C-ITS for platooning and automated driving systems?
This is a very broad question, so my answer will focus on truck automation, in particular platooning. Of crucial importance is to understand that the deployment of automated driving technologies always depends critically on the business case. The trucking industry operates on small margins and investments in automated driving technologies requires a strong business case with fast return of investment. Since fuel is typically the largest operational cost for truck fleets, platooning offers a very strong business case thanks to the high potential fuel savings (~5% for the leader and ~10% for the follower), even at lower levels of automation. Platooning also have significant potential safety benefits, although these are harder to quantify. In my view, the key challenge in the deployment of automated driving systems, including Level 1 systems such as DAPT, is to test systems in the real world and verify that they are able to handle all possible (“edge”) cases and do not introduce any unintended effects on driver behavior or the surrounding traffic. Of key importance will also be to evaluate potential safety benefits. This will require extensive naturalistic driving data collection and novel data analytics techniques including virtual testing (i.e., computer simulation).