ADVANCED DRIVER ASSISTANCE SYSTEM CAR FOLLOWING MODEL OPTIMIZATION FRAMEWORK USING GENETIC ALGORITHM IMPLEMENTED IN SUMO TRAFFIC SIMULATION

As advanced driver-assistance systems (ADAS) such as smart cruise control and lane keeping have become common technologies, self-driving above SAE level 3 are being competitively developed by major automobile manufacturers, autonomous vehicles (AVs) will prevail in the near future traffic network. In particular, evasive action algorithms with collision detec- tion by sensors and faster braking response will enable AVs to drive with a shorter gap at higher speeds which has not been possible with human drivers. Such technologies will be able to improve current traffic performance as long as raising concerns on safety are addressed. Therefore, there have been efforts to improve understanding between stakeholders such as regulatory authorities and developers to draw a consensus about autonomous driving stan- dard and regulations. Meanwhile, a mixed traffic network with human driving vehicles and AVs will show transient system behavior based on penetration rate of AVs thereby requiring different optimal AV settings. We are interested in understanding this system behavior over transitional period to achieve an optimal traffic performance with safety as a hard constraint. We investigate the system behavior with agent-based simulation with different penetration rates by mixing of human-driving and AV vehicle models, identify the key parameters of ADAS algorithms for traffic flow, and find the optimal parameter set per penetration rate by using genetic algorithm (GA). Simulation results with optimal parameter values reveal improvement in average traffic performance measures such as flow (5.6% increase), speed (4.9% increase), density (15.9% decrease), and waiting time (48.2% decrease). We provide simulation examples and discuss the implication of the optimal parameter values for both traffic control authorities and AV developers during the transitional period.