Prediction-Based Reachability Analysis for Collision Risk Assessment on
Highways
- URL: http://arxiv.org/abs/2205.01357v1
- Date: Tue, 3 May 2022 07:58:02 GMT
- Title: Prediction-Based Reachability Analysis for Collision Risk Assessment on
Highways
- Authors: Xinwei Wang, Zirui Li, Javier Alonso-Mora, Meng Wang
- Abstract summary: This paper introduces a prediction-based collision risk assessment approach on highways.
We develop an acceleration prediction model, which provides multi-modal probabilistic acceleration distributions to propagate vehicle states.
The proposed collision detection approach is agile and effective to identify the collision in cut-in crash events.
- Score: 18.18842948832662
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Real-time safety systems are crucial components of intelligent vehicles. This
paper introduces a prediction-based collision risk assessment approach on
highways. Given a point mass vehicle dynamics system, a stochastic forward
reachable set considering two-dimensional motion with vehicle state probability
distributions is firstly established. We then develop an acceleration
prediction model, which provides multi-modal probabilistic acceleration
distributions to propagate vehicle states. The collision probability is
calculated by summing up the probabilities of the states where two vehicles
spatially overlap. Simulation results show that the prediction model has
superior performance in terms of vehicle motion position errors, and the
proposed collision detection approach is agile and effective to identify the
collision in cut-in crash events.
Related papers
- Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections [12.812518632907771]
We present a novel framework that detects preemptively collisions at urban crossroads.
We exploit the Multi-access Edge Computing platform of 5G networks.
arXiv Detail & Related papers (2024-04-22T18:45:40Z) - SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Controllable Adversaries [94.84458417662407]
We introduce SAFE-SIM, a novel diffusion-based controllable closed-loop safety-critical simulation framework.
We develop a novel approach to simulate safety-critical scenarios through an adversarial term in the denoising process.
We validate our framework empirically using the NuScenes dataset, demonstrating improvements in both realism and controllability.
arXiv Detail & Related papers (2023-12-31T04:14:43Z) - Trajectory Prediction with Observations of Variable-Length for Motion
Planning in Highway Merging scenarios [5.193470362635256]
Existing methods cannot initiate prediction for a vehicle unless observed for a fixed duration of two or more seconds.
This paper proposes a novel transformer-based trajectory prediction approach, specifically trained to handle any observation length larger than one frame.
We perform a comprehensive evaluation of the proposed method using two large-scale highway trajectory datasets.
arXiv Detail & Related papers (2023-06-08T18:03:48Z) - P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving [28.948224519638913]
We evaluate state-of-the-art predictors through novel conflict-related metrics.
We propose a simple but effective alternative that combines a physics-based trajectory generator and a learning-based predictor.
Our predictor, P4P, achieves superior performance over existing learning-based predictors in realistic interactive driving scenarios.
arXiv Detail & Related papers (2022-11-03T07:51:40Z) - Connecting Surrogate Safety Measures to Crash Probablity via Causal
Probabilistic Time Series Prediction [0.0]
This paper proposes a method to connect surrogate safety measures to crash probability using probabilistic time series prediction.
The method used sequences of speed, acceleration and time-to-collision to estimate the probability density functions of those variables.
The estimated sequence is accurate and the conditional crash probability shows the effectiveness of evasive action to avoid crashes in a counterfactual experiment.
arXiv Detail & Related papers (2022-10-04T04:08:59Z) - AdvDO: Realistic Adversarial Attacks for Trajectory Prediction [87.96767885419423]
Trajectory prediction is essential for autonomous vehicles to plan correct and safe driving behaviors.
We devise an optimization-based adversarial attack framework to generate realistic adversarial trajectories.
Our attack can lead an AV to drive off road or collide into other vehicles in simulation.
arXiv Detail & Related papers (2022-09-19T03:34:59Z) - Safety-aware Motion Prediction with Unseen Vehicles for Autonomous
Driving [104.32241082170044]
We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving.
Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map.
Our approach is the first one that can predict the existence of unseen vehicles in most cases.
arXiv Detail & Related papers (2021-09-03T13:33:33Z) - Congestion-aware Multi-agent Trajectory Prediction for Collision
Avoidance [110.63037190641414]
We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework.
By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories.
In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
arXiv Detail & Related papers (2021-03-26T02:42:33Z) - Generating and Characterizing Scenarios for Safety Testing of Autonomous
Vehicles [86.9067793493874]
We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator.
We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project.
We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
arXiv Detail & Related papers (2021-03-12T17:00:23Z) - Deep Learning with Attention Mechanism for Predicting Driver Intention
at Intersection [2.1699196439348265]
The proposed solution is promising to be applied in advanced driver assistance systems (ADAS) and as part of active safety system of autonomous vehicles.
The performance of the proposed approach is evaluated on a naturalistic driving dataset and results show that our method achieves high accuracy as well as outperforms other methods.
arXiv Detail & Related papers (2020-06-10T16:12:00Z) - The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
Roads have well defined geometries, topologies, and traffic rules.
In this paper we propose to incorporate structured priors as a loss function.
We demonstrate the effectiveness of our approach on real-world self-driving datasets.
arXiv Detail & Related papers (2020-06-04T03:56:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.