Field-based Threat Assessment and Control

We are currently working on active navigation systems for autonomous or semi-autonomous vehicles based on the notion of “fields of safe travel” or “homotopy class”. The motivation behind this approach is the assumption that human drivers tend to operate vehicles within a field of safe travel rather than rigidly follow a specific path. In other words, human first makes decisions about avoidance strategy or desired goals, then tries to keep the vehicle within a field of safe travel corresponding to decision made. Based on this, we propose a framework that relies on identification and analysis of candidate fields, each of which can be interpreted to contain a path homotopy. Such navigation system tries to keep consistency of homotopy selection with human’s decision based on estimation of human’s intent or recommendation system. Once focus of planning and control comes down to each single field of safe travel, the system ensures safety while still allowing control freedom of human operator. The appropriate design of intervention time for this objective is achieve by measure of threat. The automated system ideally operates only during instances of significant threat: it should give a driver full control of the vehicle in “low threat” situations but apply appropriate levels of computer-controlled actuator effort during “high threat” situations. This approach preserves the freedom of motion of the human driver when he remains within the desirable navigable corridors and adjust its trajectory when its predicted future state falls out of these fields or when the lowest threat exceeds some threshold.

The three key points of the semi autonomous system are the following:

  1. Identification of fields of safe travel based on the sensory information related to the vehicle surroundings (nearby vehicles, pedestrians, road edges).
  2. Threat assessment or characterization of each field of safe travel.
  3. The question of when and how to intervene based on a threat in a way to achieve both of safety and respect of operator’s decision or preferences. 

These three questions are related to each other and appropriate assessment of threat directly affects the performance of the system. In the current stage of the research, two different approaches are investigated: approach A. assessment based on optimal trajectory (workspace-based), approach B. assessment based on margin of control freedom (input space-based).


ApproachA assesses the threat of the best-case scenario. The cost of the optimal trajectory is the minimum required cost for ensuring safety, so the system intervenes only when even the optimal maneuver has a high cost for achieving safety. The problem comes down to a design of cost function and computationally efficient optimization. The cost function could be designed to accommodate severeness of maneuver such as longitudinal and lateral acceleration, sideslip angles although verification of the hypothesis is required. Given a cost function, this work is investigating a divide-and-conquer strategy of utilizing homotopy identification framework for efficient optimization in a sense that optimal trajectories for each of homotopy class should contain the global optimal trajectory.


In approach B, the threat to a vehicle is assumed to be proportional to the freedom of control afforded by a particular field. This implies that (for instance) drivers tend to prefer to navigate through regions that are wide and exhibit low curvature. To characterize threat in each candidate field, a set of feasible trajectories is evaluated rather than a single trajectory as in the first approach. For this purpose, a lattice is constructed via sampling either in input or state space. This sampling method enables identification of sets of feasible trajectories associated with each field; from which the metrics related to control freedom can be derived. 




Karl Iagnemma's picture
Karl Iagnemma
Robotic Mobility Group
Massachusetts Institute of Technology


Jack Riderhof
Dynamics and Control Systems Laboratory
Georgia Tech



Constantin, A., J. Park, and K. Iagnemma, "A margin-based approach to threat assessment for autonomous highway navigation", Intelligent Vehicles Symposium Proceedings, 2014 IEEE, June, 2014. PDF icon ivs2014.Constantin.Park_.Iagnemma.final_.pdf (2.95 MB)


Karumanchi, S. B., K. Iagnemma, and S. Scheding, "Mobility Erosion: High speed motion safety for mobile robots operating in off-road terrain", Robotics and Automation (ICRA), 2013 IEEE International Conference on, May, 2013. PDF icon icra2013.Karumanchi.Iagnemma.Scheding.accepted.pdf (2.44 MB)
Jeon, J H., R. V. Cowlagi, S. S. C. Peters, S. S. Karaman, E. Frazzoli, P. Tsiotras, and K. Iagnemma, "Optimal motion planning with the half-car dynamical model for autonomous high-speed driving", American Control Conference (ACC), 2013, June, 2013. PDF icon acc2013.Jeon_.Cowlagi.ea_.final_.pdf (613.48 KB)
Anderson, S., S. B. Karumanchi, K. Iagnemma, and J. M. Walker, "The intelligent copilot: A constraint-based approach to shared-adaptive control of ground vehicles", Intelligent Transportation Systems Magazine, IEEE, vol. 5, pp. 45-54, Summer, 2013. PDF icon itsm13.Anderson.Karumanchi.Iagnemma.Walker.printed.pdf (2.5 MB)


Anderson, S.. J., S. B. Karumanchi, and K. Iagnemma, "Constraint-based planning and control for safe, semi-autonomous operation of vehicles.", Intelligent Vehicles Symposium: IEEE, 2012. PDF icon ieeeiv2012.Anderson.Karumanchi.Iagnemma.printed.pdf (1.66 MB)
Peters, S. S. C., S.. J. Anderson, T. Pilutti, E. H. Tseng, and K. Iagnemma, "Threat-based hazard avoidance for semi- autonomous vehicles using nonlinear model predictive control", IEEE Transactions on Control Systems Technology, 2012. PDF icon cst12.Peters.Anderson.Pilutti.etal_.submitted.pdf (847.44 KB)
Anderson, S.. J., S. B. Karumanchi, B. Johnson, V. Perlin, M. Rohde, and K. Iagnemma, Constraint-based semi-autonomy for unmanned ground vehicles using local sensing, , pp. 83870K-83870K-8, 2012. PDF icon spie2012.Anderson.Iagnemma.printed.pdf (2.11 MB)


Arndt, D., J. E. Bobrow, S. S. C. Peters, K. Iagnemma, and S. Dubowsky, "Two-Wheel Self-Balancing of a Four-Wheeled Vehicle", IEEE Control Systems Magazine, April 2011. PDF icon csm11.Arndt_.Bebrow.Peters.etal_.printed.pdf (2.12 MB)

Dynamics and Control Systems Lab


Robotic Mobility Group


Aerospace Robotics and Embedded Systems Laboratory

About us

We are three research groups from Georgia Tech, the Massachusetts Institute of Technology, and the University of Southern California, collaborating to perform basic research on high-speed autonomous driving.  We are most interested in researching biologically-inspired methods in the realms of both perception and control.


This work was supported by the Army Research Office under MURI Award W911NF-11-1-0046.