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We propose a Stochastic MPC (SMPC) approach for autonomous driving which incorporates multi-modal, interaction-aware predictions of surrounding vehicles. For each mode, vehicle motion predictions are obtained by a control model described using a basis of fixed features with unknown weights. The proposed SMPC formulation finds optimal controls which serves two purposes: 1) reducing conservatism of the SMPC by optimizing over parameterized control laws and 2) prediction and estimation of feature weights used in interaction-aware modeling using Kalman filtering. The proposed approach is demonstrated on a longitudinal control example, with uncertainties in predictions of the autonomous and surrounding vehicles., Accepted to AVEC'22

Movie comparing predicted valve displacements

Supplementary Fig. 1 Time-varying velocity and their respective profiles for SAV, NAV, and TAVR respectively. (A) Model-predicted time-varying velocity extracted from ascending aorta proximally to AVs. (B-D) shows the cross-sectional velocity profiles at early-, mid-accelerating-, peak-, late-decelerating, and end- systole for SAV, NAV, and TAVR, respectively. Supplementary Fig. 2 Grid resolution used for each of the SAV, NAV, and TAVR models. Simulation was started with a mesh size of ���15 million cells. As flow dynamics became complex, local mesh refinement resulted in a constant increase of mesh size, particularly in the ascending aorta (refer Fig. 1). Near end-systole, total grid size was ���19 million.

As autonomous vehicles (AVs) inch closer to reality, a central requirement for acceptance will be earning the trust of humans in everyday driving situations. In particular, the interaction between AVs and pedestrians is of high importance, as every human is a pedestrian at some point of the day. This paper considers the interaction of a pedestrian and an autonomous vehicle at a mid-block, unsignalized intersection where there is ambiguity over when the pedestrian should cross and when and how the vehicle should yield. By modeling pedestrian behavior through the concept of gap acceptance, the authors show that a hybrid controller with just four distinct modes allows an autonomous vehicle to successfully interact with a pedestrian across a continuous spectrum of possible crosswalk entry behaviors. The controller is validated through extensive simulation and compared to an alternate POMDP solution and experimental results are provided on a research vehicle for a virtual pedestrian.

An excerpt from Reverse Innovation in Health Care: How To Make Value-Based Delivery Work

Entrevista a Vijay Govindarajan, un dels millors experts al món en estratègia i innovació.

Interview to Vijay Govindarajan, one of the world's top experts in strategy and innovation.