  Widespread adoption of autonomous vehicles will not become a reality until
solutions are developed that enable these intelligent agents to co-exist with
humans. This includes safely and efficiently interacting with human-driven
vehicles, especially in both conflictive and competitive scenarios. We build up
on the prior work on socially-aware navigation and borrow the concept of social
value orientation from psychology -- that formalizes how much importance a
person allocates to the welfare of others -- in order to induce altruistic
behavior in autonomous driving. In contrast with existing works that explicitly
model the behavior of human drivers and rely on their expected response to
create opportunities for cooperation, our Sympathetic Cooperative Driving
(SymCoDrive) paradigm trains altruistic agents that realize safe and smooth
traffic flow in competitive driving scenarios only from experiential learning
and without any explicit coordination. We demonstrate a significant improvement
in both safety and traffic-level metrics as a result of this altruistic
behavior and importantly conclude that the level of altruism in agents requires
proper tuning as agents that are too altruistic also lead to sub-optimal
traffic flow. The code and supplementary material are available at:
https://symcodrive.toghi.net/
