Gritschneder et al., 2016 - Google Patents
Adaptive learning based on guided exploration for decision making at roundaboutsGritschneder et al., 2016
- Document ID
- 15343070175121030088
- Author
- Gritschneder F
- Hatzelmann P
- Thom M
- Kunz F
- Dietmayer K
- Publication year
- Publication venue
- 2016 IEEE Intelligent Vehicles Symposium (IV)
External Links
Snippet
This paper proposes a learning-based behavior generation approach for automated vehicles which is adapted sequentially. Instead of engineering behavioral policies for a variety of individual traffic situations by hand, our approach concentrates on a general …
- 230000003044 adaptive 0 title description 4
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0295—Fleet control by at least one leading vehicle of the fleet
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0011—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D2201/00—Application
- G05D2201/02—Control of position of land vehicles
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Teng et al. | Motion planning for autonomous driving: The state of the art and future perspectives | |
Stahl et al. | Multilayer graph-based trajectory planning for race vehicles in dynamic scenarios | |
Sadat et al. | Jointly learnable behavior and trajectory planning for self-driving vehicles | |
Li et al. | Real-time trajectory planning for autonomous urban driving: Framework, algorithms, and verifications | |
Lu et al. | Learning driver-specific behavior for overtaking: A combined learning framework | |
Xia et al. | Neural inverse reinforcement learning in autonomous navigation | |
Gritschneder et al. | Adaptive learning based on guided exploration for decision making at roundabouts | |
Wu et al. | Route planning and tracking control of an intelligent automatic unmanned transportation system based on dynamic nonlinear model predictive control | |
Ben-Messaoud et al. | Smooth obstacle avoidance path planning for autonomous vehicles | |
Lin et al. | Policy iteration based approximate dynamic programming toward autonomous driving in constrained dynamic environment | |
Muzahid et al. | Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles | |
Li et al. | Marc: Multipolicy and risk-aware contingency planning for autonomous driving | |
Lodhi et al. | Autonomous vehicular overtaking maneuver: A survey and taxonomy | |
Liu et al. | The robustly-safe automated driving system for enhanced active safety | |
Lakhal et al. | Safe and adaptive autonomous navigation under uncertainty based on sequential waypoints and reachability analysis | |
Nair et al. | Predictive control for autonomous driving with uncertain, multimodal predictions | |
Cai et al. | Rule‐constrained reinforcement learning control for autonomous vehicle left turn at unsignalized intersection | |
Diehl et al. | Energy-based potential games for joint motion forecasting and control | |
Wang et al. | Safe Reinforcement Learning for Automated Vehicles via Online Reachability Analysis | |
Batkovic | Enabling Safe Autonomous Driving in Uncertain Environments | |
Iberraken et al. | Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches | |
Li et al. | Decision making for autonomous vehicles | |
Villagra et al. | Decision-Making Techniques for Autonomous Vehicles | |
Ventura | Safe and flexible hybrid control architecture for the navigation in formation of a group of vehicles | |
Skoglund | Risk-aware autonomous driving using POMDPs and responsibility-sensitive safety |