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AIP-AVP: Auction-Inspired Pricing for Long-Range Autonomous Valet Parking Based on Hierarchical Optimization
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DOI：10.1109/TIV.2026.3690440.png)
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Abstract
En 中文
Long-range Autonomous Valet Parking (LAVP) is a self-driving technology, that enables autonomous vehicles (AV) to automatically find a parking lot (PL), pass through the drop-off/pick-up (D/P) point, and stop when the user is away. However, on the one hand, the existing proposed LAVP system first selects D/P points based on the distance from origins of AVs to their destinations, and then chooses PLs for AVs to park. This process ignores that the choice of D/P points will affect the occupancy of PLs, thus decreasing the efficiency of LAVP systems and increasing the average waiting time for parking of AVs. On the other hand, most LAVP systems adopt traditional static or dynamic pricing strategies, which may lead to overpriced PLs. This ultimately leads to an increase in the total parking cost of users, and the PL owner cannot obtain higher profits. As a result, this paper proposes an auction-inspired pricing mechanism for LAVP based on a hierarchical optimization framework. This proposed system reduces the average waiting time at PLs for AVs, thus reducing the total parking cost of users. At the end of this paper, simulation results show that our system achieves lower total parking costs for users, reduces average waiting time for parking, and maintains balanced occupancy of PLs compared with baseline methods.
Keywords:
Long-range autonomous valet parking (LAVP)
autonomous vehicles
auction-inspired
pricing
hierarchical optimization
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