Despite the leaps in the autonomous driving domain, autonomous vehicles (AVs) are still inefficient and limited in terms of cooperating with each other or coordinating with vehicles operated by humans. A group of autonomous and human-driven vehicles (HVs) which work together to optimize an altruistic social utility -- as opposed to the egoistic individual utility -- can co-exist seamlessly and assure safety and efficiency on the road. Achieving this mission is challenging in the absence of explicit coordination among agents . Additionally, existence of humans in mixed-autonomy environments create social dilemmas as they are known to be heterogeneous in social preference and their behavior is hard to predict by nature . Formally, we model an AV's maneuver planning in mixed-autonomy traffic as a partially-observable stochastic game and attempt to derive optimal policies that lead to socially-desirable outcomes using our multi-agent reinforcement learning framework. We introduce a quantitative representation of the AVs' social value orientation and design a distributed reward structure that induces altruism into their decision making process. Our trained altruistic AVs are able to form alliances, guide the traffic, and affect the behavior of the HVs to handle conflictive and competitive driving scenarios. As a case study, we compare egoistic AVs to our altruistic autonomous agents in a highway merging case study and demonstrate a significant improvement in the number of successful merges as well as the overall traffic flow and safety.
Despite the advances in the autonomous driving domain, autonomous vehicles (AVs) are still inefficient and limited in terms of cooperating with each other or coordinating with vehicles operated by humans. A group of autonomous and human-driven vehicles (HVs) which work together to optimize an altruistic social utility -- as opposed to the egoistic individual utility -- can co-exist seamlessly and assure safety and efficiency on the road. Achieving this mission without explicit coordination among agents is challenging, mainly due to the difficulty of predicting the behavior of humans with heterogeneous preferences in mixed-autonomy environments . Formally, we model an AV's maneuver planning in mixed-autonomy traffic as a partially-observable stochastic game and attempt to derive optimal policies that lead to socially-desirable outcomes using a multi-agent reinforcement learning framework. We introduce a quantitative representation of the AVs' social preferences and design a distributed reward structure that induces altruism into their decision making process. Our altruistic AVs are able to form alliances, guide the traffic, and affect the behavior of the HVs to handle competitive driving scenarios. As a case study, we compare egoistic AVs to our altruistic autonomous agents in a highway merging setting and demonstrate the emerging behaviors that lead to a noticeable improvement in the number of successful merges as well as the overall traffic flow and safety.
R
leaps
advances
R
is challenging in the absence of
without
R
. Additionally, existence of humans
is challenging, mainly due to the difficulty of predicting the behavior of humans with heterogeneous preferences
D
create social dilemmas as they are known to be heterogeneous in social preference and their behavior is hard to predict by nature
R
our
a
R
value orientation
preferences
D
trained
D
conflictive and
R
case study and demonstrate a significant
setting and demonstrate the emerging behaviors that lead to a noticeable