REVIEW 8 cited by
Adaptive Field Effect Planner for Safe Interactive Autonomous Driving on Curved Roads
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Adaptive Field Effect Planner for Safe Interactive Autonomous Driving on Curved Roads
read the original abstract
Autonomous driving has garnered significant attention for its potential to improve safety, traffic efficiency, and user convenience. However, the dynamic and complex nature of interactive driving poses significant challenges, including the need to navigate non-linear road geometries, handle dynamic obstacles, and meet stringent safety and comfort requirements. Traditional approaches, such as artificial potential fields (APF), often fall short in addressing these complexities independently, necessitating the development of integrated and adaptive frameworks. This paper presents a novel approach to autonomous vehicle navigation that integrates artificial potential fields, Frenet coordinates, and improved particle swarm optimization (IPSO). A dynamic risk field, adapted from traditional APF, is proposed to ensure interactive safety by quantifying risks and dynamically adjusting lane-changing intentions based on surrounding vehicle behavior. Frenet coordinates are utilized to simplify trajectory planning on non-straight roads, while an enhanced quintic polynomial trajectory generator ensures smooth and comfortable path transitions. Additionally, an IPSO algorithm optimizes trajectory selection in real time, balancing safety and user comfort within a feasible input range. The proposed framework is validated through extensive simulations and real-world scenarios, demonstrating its ability to navigate complex traffic environments, maintain safety margins, and generate smooth, dynamically feasible trajectories.
Forward citations
Cited by 8 Pith papers
-
Enhanced Mean Field Game for Interactive Decision-Making with Varied Stylish Multi-Vehicles
A mean-field-game lane-change planner with six hand-set driving styles claims collision-free simulation results, but its promised NGSIM calibration and baseline comparisons do not appear in the body.
-
Mean Field Game-Based Interactive Trajectory Planning Using Physics-Inspired Unified Potential Fields
An autonomous driving planner that merges mean-field game interactions and potential fields into one unified field, claiming Nash equilibrium convergence but providing an incomplete proof and limited simulation evidence.
-
Attention and Risk-Aware Decision Framework for Safe Autonomous Driving
An improved PPO for highway driving uses a hybrid risk field, attention modules, a balanced reward, and a rule-based safety filter to reduce collisions and speed up training in simulation.
-
Adaptive Evolution Factor Risk Ellipse Framework for Reliable and Safe Autonomous Driving
An adaptive risk-field-plus-MPC controller with a sigmoid evolution factor and TTC/TWH-based risk ellipses is claimed to achieve collision-free overtaking and lane changes in simulation.
-
A Risk-aware Spatial-temporal Trajectory Planning Framework for Autonomous Vehicles Using QP-MPC and Dynamic Hazard Fields
Integrates a dynamic hazard field into QP-MPC trajectory planning and reports smoother, safer lane changes, overtaking, and intersection crossings in simulation.
-
Safe and Efficient Lane-Changing for Autonomous Vehicles: An Improved Double Quintic Polynomial Approach with Time-to-Collision Evaluation
An optimization-based double quintic lane-change planner that penalizes low time-to-collision is proposed and tested in simulation.
-
Leveraging Vision-Language Large Models for Interpretable Video Action Recognition with Semantic Tokenization
LVLM-VAR transforms video into 'semantic action tokens' and uses a LoRA-tuned vision-language model to classify actions and generate explanations, reporting 94.1% on NTU RGB+D X-Sub.
-
Scenario-based Decision-making Using Game Theory for Interactive Autonomous Driving: A Survey
A scenario-based survey of game-theoretic autonomous driving decision-making that claims comprehensiveness but is undermined by a non-systematic methodology and numerous internal errors.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.