Zhang et al., 2024 - Google Patents
An NSGA-II-based multi-objective trajectory planning method for autonomous drivingZhang et al., 2024
- Document ID
- 7365944904024311749
- Author
- Zhang D
- Zhang Z
- Li Y
- Wang Y
- Zhang W
- Zhu Y
- Publication year
- Publication venue
- 2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
External Links
Snippet
With the rapid development of a new generation of information and communication technologies such as artificial intelligence, big data, and the Internet of Things, the automotive industry is rapidly evolving in the direction of electrification, intelligence, and …
- 238000000034 method 0 title abstract description 49
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110749333B (en) | Unmanned vehicle motion planning method based on multi-objective optimization | |
| Li et al. | An optimization-based path planning approach for autonomous vehicles using the DynEFWA-artificial potential field | |
| CN107168305B (en) | Bezier and VFH-based unmanned vehicle track planning method under intersection scene | |
| González et al. | A review of motion planning techniques for automated vehicles | |
| CN105549597B (en) | A kind of unmanned vehicle dynamic path planning method based on environmental uncertainty | |
| WO2018176593A1 (en) | Local obstacle avoidance path planning method for unmanned bicycle | |
| Lin et al. | Traffic signal optimization based on fuzzy control and differential evolution algorithm | |
| Jayawardana et al. | Learning eco-driving strategies at signalized intersections | |
| Hu et al. | Event-triggered model predictive adaptive dynamic programming for road intersection path planning of unmanned ground vehicle | |
| Gao et al. | Accurate pseudospectral optimization of nonlinear model predictive control for high-performance motion planning | |
| Wang et al. | Research on AGV task path planning based on improved A* algorithm | |
| Huang et al. | Trajectory planning in Frenet frame via multi-objective optimization | |
| Liu et al. | Impact of sharing driving attitude information: A quantitative study on lane changing | |
| Lodhi et al. | Autonomous vehicular overtaking maneuver: A survey and taxonomy | |
| Karlsson et al. | Intention-aware motion planning with road rules | |
| Guan et al. | Learn collision-free self-driving skills at urban intersections with model-based reinforcement learning | |
| Ren et al. | Self-learned intelligence for integrated decision and control of automated vehicles at signalized intersections | |
| Wang et al. | Towards the next level of vehicle automation through cooperative driving: A roadmap from planning and control perspective | |
| Yu et al. | Hierarchical framework integrating rapidly-exploring random tree with deep reinforcement learning for autonomous vehicle | |
| Wang et al. | A Risk-field Based Motion Planning Method for Multi-vehicle Conflict Scenario | |
| Pauls et al. | Real-time cooperative motion planning using efficient model predictive contouring control | |
| Zhang et al. | An NSGA-II-based multi-objective trajectory planning method for autonomous driving | |
| Lattarulo et al. | Fast real-time trajectory planning method with 3rd-order curve optimization for automated vehicles | |
| Li et al. | Decision making for autonomous vehicles | |
| Huang et al. | General Optimal Trajectory Planning: Enabling Autonomous Vehicles with the Principle of Least Action |