# Navigating Uncertainty: The Role of Short-Term Trajectory Prediction in Autonomous Vehicle Safety

arXiv: https://arxiv.org/abs/2307.05288 · HTML: https://mlsanity.com/paper/2307.05288/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 9 of 9)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Traj-MAE: Masked Autoencoders for Trajectory Prediction](https://mlsanity.com/paper/2303.06697/) (2023)
- [Enhanced Mobility With Connectivity and Automation: A Review of Shared Autonomous Vehicle Systems](https://mlsanity.com/paper/1905.12602/) (2019)
- [Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging](https://mlsanity.com/paper/2304.00397/) (2023)
- [Long-term Prediction of Vehicle Behavior using Short-term Uncertainty-aware Trajectories and High-definition Maps](https://mlsanity.com/paper/2003.06143/) (2020)
- [Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process](https://mlsanity.com/paper/1910.08102/) (2019)

## Built on by (most cited first, 3 of 3)

- [BEVSeg2TP: Surround View Camera Bird's-Eye-View Based Joint Vehicle Segmentation and Ego Vehicle Trajectory Prediction](https://mlsanity.com/paper/2312.13081/) (2023)
- [Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach](https://mlsanity.com/paper/2312.13104/) (2023)
- [MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles](https://mlsanity.com/paper/2407.05811/) (2024)

## Abstract (arXiv)

Autonomous vehicles require accurate and reliable short-term trajectory predictions for safe and efficient driving. While most commercial automated vehicles currently use state machine-based algorithms for trajectory forecasting, recent efforts have focused on end-to-end data-driven systems. Often, the design of these models is limited by the availability of datasets, which are typically restricted to generic scenarios. To address this limitation, we have developed a synthetic dataset for short-term trajectory prediction tasks using the CARLA simulator. This dataset is extensive and incorporates what is considered complex scenarios - pedestrians crossing the road, vehicles overtaking - and comprises 6000 perspective view images with corresponding IMU and odometry information for each frame. Furthermore, an end-to-end short-term trajectory prediction model using convolutional neural networks (CNN) and long short-term memory (LSTM) networks has also been developed. This model can handle corner cases, such as slowing down near zebra crossings and stopping when pedestrians cross the road, without the need for explicit encoding of the surrounding environment. In an effort to accelerate this research and assist others, we are releasing our dataset and model to the research community. Our datasets are publicly available on https://github.com/sharmasushil/Navigating-Uncertainty-Trajectory-Prediction .