Implicit Sensing in Traffic Optimization: Advanced Deep Reinforcement
  Learning Techniques
Emanuel Figetakis Yahuza Bello Ahmed Refaey Lei Lei Medhat Moussa
A sudden roadblock on highways due to many reasons such as road maintenance,
accidents, and car repair is a common situation we encounter almost daily.
Autonomous Vehicles (AVs) equipped with sensors that can acquire vehicle
dynamics such as speed, acceleration, and location can make intelligent
decisions to change lanes before reaching a roadblock. A number of literature
studies have examined car-following models and lane-changing models. However,
only a few studies proposed an integrated car-following and lane-changing
model, which has the potential to model practical driving maneuvers. Hence, in
this paper, we present an integrated car-following and lane-changing
decision-control system based on Deep Reinforcement Learning (DRL) to address
this issue. Specifically, we consider a scenario where sudden construction work
will be carried out along a highway. We model the scenario as a Markov Decision
Process (MDP) and employ the well-known DQN algorithm to train the RL agent to
make the appropriate decision accordingly (i.e., either stay in the same lane
or change lanes). To overcome the delay and computational requirement of DRL
algorithms, we adopt an MEC-assisted architecture where the RL agents are
trained on MEC servers. We utilize the highly reputable SUMO simulator and
OPENAI GYM to evaluate the performance of the proposed model under two
policies; {\epsilon}-greedy policy and Boltzmann policy. The results
unequivocally demonstrate that the DQN agent trained using the
{\epsilon}-greedy policy significantly outperforms the one trained with the
Boltzmann policy.