Real-Time Constrained Trajectory Planning and Vehicle Control for Proactive Autonomous Driving With Road Users
DOI: 10.23919/ecc.2019.8796099
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the challenge of safe and proactive autonomous driving in complex urban environments where non-autonomous road users, such as pedestrians, are present. The authors argue that existing trajectory planning methods often decouple longitudinal and lateral control or fail to adequately account for the dynamic, stochastic behavior of pedestrians, leading to conservative or unsafe plans. To resolve this, the study proposes a real-time framework that simultaneously handles trajectory planning and vehicle control using Model Predictive Control (MPC). The primary motivation is to ensure collision avoidance while maintaining passenger comfort by predicting pedestrian motions and integrating them directly into the vehicle’s control constraints. The proposed framework utilizes a kinematic bicycle model for the vehicle and an environment-aware predictor for pedestrians, which generates probabilistic trajectories based on road geometry and reference paths. The core innovation lies in transforming these predicted pedestrian positions and their associated uncertainty (covariance ellipses) into linear constraints within the MPC formulation. This allows the controller to plan a time-parameterized trajectory that actively avoids predicted pedestrian zones. The nonlinear MPC problem is solved efficiently using a Sequential Quadratic Programming (SQP) approach combined with a Real-Time Iteration (RTI) scheme, ensuring low computational complexity. Additionally, the authors introduce a novel reference generation method that adapts to obstacles by integrating velocity profiles rather than shifting fixed references, preventing aggressive recovery behaviors when the vehicle slows down. The framework was validated through both simulations and real-world experiments. Simulations utilized real pedestrian trajectory data to test the controller’s ability to navigate intersections safely. Experimental validation was conducted on a Volvo XC90 at the Astazero test track, featuring a simulated pedestrian crossing scenario. The vehicle was controlled via an external computer running the MPC algorithm, with state estimation provided by an Extended Kalman Filter and high-accuracy GPS. The experimental setup accounted for significant input delays (approximately 300ms) and actuator limitations by augmenting the state space with time-delayed states and implementing speed-dependent steering constraints. Results demonstrate that the proposed controller is stable and effective even under significant input delays and actuator constraints. The system maintained very low computational times, confirming its suitability for real-time applications. The vehicle successfully executed maneuvers, such as 90-degree turns, while proactively adjusting its trajectory to avoid the simulated pedestrian without coming to a complete stop unless necessary. The study concludes that unifying motion planning and control within an MPC framework, coupled with accurate pedestrian prediction and adaptive reference generation, provides a robust solution for autonomous driving in urban scenarios. This approach ensures safety and comfort by treating pedestrian avoidance as a hard constraint rather than a soft penalty, offering a significant improvement over decoupled planning methods.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-24 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-24 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Theoretical Contribution: computational model