Altruistic Control of Connected Automated Vehicles in Mixed-Autonomy Multi-Lane Highway Traffic
Paper i proceeding, 2020

We consider the problem of altruistic control of connected automated vehicles (CAVs) on mixed-autonomy multi-lane highways to mitigate moving traffic jams resulting from car-following dynamics of human-driven vehicles (HDVs). In most of the existing studies on CAVs in multi-lane settings, vehicle controller design philosophy is based on a selfish driving strategy that exclusively addresses the ego vehicle objectives. To improve overall traffic smoothness, we propose an altruistic control strategy for CAVs that aims to maximize the driving comfort and traffic efficiency of both the ego vehicle and surrounding HDVs. We formulate the problem of altruistic control under a model predictive control (MPC) framework to optimize acceleration and lane change sequences of CAVs. In order to efficiently solve the resulting non-convex mixed-integer nonlinear programming (MINLP) problem, we decompose it into three non-convex subproblems, each of which can be transformed into a convex quadratic program via penalty based reformulation of the optimal velocity with relative velocity (OVRV) car-following model. Simulation results demonstrate significant improvements in traffic flow via altruistic CAV actions over selfish strategies on both single- and multi-lane roads.

Intelligent transportation systems

Autonomous Vehicles

Motion control


Furkan Keskin

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk, Kommunikationssystem

Bile Peng

Technische Universität Braunschweig

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk, Kommunikationssystem


24058963 (eISSN)

Vol. 53 2 14966-14971

IFAC World Congress 2020
Online, ,

Safety aware vehicular routing using on-car sensors

Chalmers, 2017-08-15 -- .








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