  This study focuses on a crucial task in the field of autonomous driving,
autonomous lane change. Autonomous lane change plays a pivotal role in
improving traffic flow, alleviating driver burden, and reducing the risk of
traffic accidents. However, due to the complexity and uncertainty of
lane-change scenarios, the functionality of autonomous lane change still faces
challenges. In this research, we conducted autonomous lane-change simulations
using both deep reinforcement learning (DRL) and model predictive control
(MPC). Specifically, we used the parameterized soft actor--critic (PASAC)
algorithm to train a DRL-based lane-change strategy to output both discrete
lane-change decisions and continuous longitudinal vehicle acceleration. We also
used MPC for lane selection based on the smallest predictive car-following
costs for the different lanes. For the first time, we compared the performance
of DRL and MPC in the context of lane-change decisions. The simulation results
indicated that, under the same reward/cost function and traffic flow, both MPC
and PASAC achieved a collision rate of 0%. PASAC demonstrated a comparable
performance to MPC in terms of average rewards/costs and vehicle speeds.
