Comprehensive Autonomous Vehicle Optimal Routing With Dynamic Heuristics
This work addresses routing optimization for autonomous vehicles to enhance user experience and traffic management, but it appears incremental as it builds on existing hybrid and heuristic approaches without claiming major breakthroughs.
The paper tackles the problem of optimizing routing for autonomous vehicles in mixed traffic environments by proposing a hybrid network model (OAVN) that coordinates multiple connected autonomous vehicles, resulting in improved transportation efficiency, passenger comfort, and safety with real-time adaptation to traffic conditions.
Auto manufacturers and research groups are working on autonomous driving for long period and achieved significant progress. Autonomous vehicles (AV) are expected to transform road traffic reduction from current conditions, avoiding accidents and congestion. As the implementation of an autonomous vehicle ecosystem includes complex automotive technology, ethics, passenger behaviour, traffic management policies and liability etc., the maturity of AV solutions are still evolving. The proposed model to improve AV user experience, uses a hybrid AV Network of multiple connected autonomous vehicles which communicate with each other in an environment shared by human driven vehicles. The proposed Optimal AV Network (OAVN) solution provides better coordination and optimization of autonomous vehicles, improved Transportation efficiency, improved passenger comfort and safety, real-time dynamic adaption of traffic & road conditions along with improved in-cabin assistance with inputs from various sensors. The true optimal solution for this problem, is to devise an automated guidance system for vehicles in an AV network, to reach destinations in best possible routes along with passenger comfort and safety. A custom informed search model is proposed along with other heuristic goals for better user experience. The results are analysed and compared to evaluate the effectiveness of the solution and identify gaps and future enhancements.