# Connecting the Dots: Context-Driven Motion Planning Using Symbolic Reasoning

arXiv: https://arxiv.org/abs/2303.04191 · HTML: https://mlsanity.com/paper/2303.04191/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency](https://mlsanity.com/paper/2402.03893/) (2024)
- [Towards Safer Autonomous Driving in an Open World: A Dual-Process Approach](https://mlsanity.com/paper/2610.04088/) (2026)

## Abstract (arXiv)

The introduction of highly automated vehicles on the public road may improve safety and comfort, although its success will depend on social acceptance. This requires trajectory planning methods that provide safe, proactive, and comfortable trajectories that are risk-averse, take into account predictions of other road users, and comply with traffic rules, social norms, and contextual information. To consider these criteria, in this article, we propose a non-linear model-predictive trajectory generator. The problem space is populated with risk fields. These fields are constructed using a novel application of a knowledge graph, which uses a traffic-oriented ontology to reason about the risk of objects and infrastructural elements, depending on their position, relative velocity, and classification, as well as depending on the implicit context, driven by, e.g., social norms or traffic rules. Through this novel combination, an adaptive trajectory generator is formulated which is validated in simulation through 4 use cases and 309 variations and is shown to comply with the relevant social norms, while taking minimal risk and progressing towards a goal area.