Lane-Aware Route Selection for Predictable Automated Lane Changes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current route selection systems, such as those used in car navigation, do not adequately account for lane changes beyond driver expectations, potentially leading to unexpected lane changes during automated driving.
Innovation Solution
A route selection device that identifies the current lane, searches for candidate partial routes, determines lane change locations, and selects routes with a minimum total score based on weighted criteria such as proximity to waypoints and feasibility of automated travel, aiming to minimize lane changes at unexpected locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a route is selected based on shortest travel distance or time without considering lane changes, then the route efficiency is improved, but unexpected lane changes occur beyond driver expectations
Solution Approach 1:
The system performs preliminary identification of lane positions and determination of lane change locations before route selection. By pre-calculating where lane changes will occur and assigning scores based on their predictability and automation feasibility, the system ensures that routes selected for automated driving will not require unexpected manual intervention, thus resolving the contradiction between efficiency and reliability.
Solution Approach 2:
The system introduces a scoring mechanism that evaluates lane change locations based on multiple parameters including distance to waypoint, automation feasibility, and distance from previous lane changes. By changing the selection criteria from simple distance/time optimization to a multi-parameter score-based evaluation, the system achieves both efficient routing and predictable lane changes suitable for automated driving.
2Adaptability or versatility
If lane change locations are optimized for automated travel, then the suitability for automated driving is improved, but the route may become more complex
Solution Approach 1:
The system applies different scoring weights to different aspects of lane change evaluation based on local conditions. For example, lane changes closer to waypoints receive different scoring than those farther away, and automation-feasible locations are weighted differently. This localized optimization approach improves automated driving suitability without requiring complete route restructuring, thus managing complexity.
3Measurement precision
If multiple candidate partial routes are searched and evaluated, then the route selection accuracy is improved, but the computational load increases
Solution Approach 1:
The system searches for multiple candidate partial routes from current position to waypoint, but does not evaluate all possible routes exhaustively. By limiting the search to a reasonable number of candidates and using score-based filtering, the system achieves sufficient route selection accuracy without requiring excessive computational resources, thus resolving the contradiction between precision and power consumption.
Data Source
AI summary
A route selection device includes a processor configured to identify a position of a lane on which a vehicle is traveling; search for candidate partial routes leading from a current position of the vehicle to a waypoint on a route leading from a start point to a destination, the waypoint being located between the current position and the destination; determine a lane change location where a lane change will be made for each of the candidate partial routes found by searching; and select, as a partial route, a candidate partial route having a minimum total score regarding the lane change location from the candidate partial routes found by searching. The score is weighted depending on the position of the determined lane change location or whether a lane change at the lane change location can be controlled by a travel controller.