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Analysing driving efficiency of mandatory lane change decision for autonomous vehicles

Analysing driving efficiency of mandatory lane change decision for autonomous vehicles

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Mandatory lane change (MLC) is a critical step in formulating global route of an autonomous vehicle on the urban road network. Improper MLC decisions on arterial roads could jeopardise efficiency (travel cost) and reliability (possibility of failed lane change). However, the existing research studies seldom investigate the optimal MLC decision at the planning level to maximise the reliability-based driving efficiency. This research aims at addressing two core strategic decision variables (MLC decision point on the road and maximum waiting time before giving up MLC). A series of simulation experiments for various scenarios are conducted for an arterial road to reveal their relationship. The results indicate that both identified decision variables inherently affect travel time spent on this road and the rate of failed MLCs, and a trade-off exists between arterial travel time and the rate of failed MLCs. Based on the simulation analysis, an analytical lane-level link performance (LLP) function is formulated to assess the impacts of MLC decisions on driving efficiency. The analysis validates that the optimal MLC strategic decision (MLC decision position and maximum waiting time) can be determined by maximising LLP function. It is promising to apply the proposed LLP function in lane-level routing algorithm in the future.

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