Towards trustworthy multi-modal motion prediction:Holistic evaluation and interpretability of outputs  

作　　者：Sandra Carrasco Limeros Sylwia Majchrowska Joakim Johnander Christoffer Petersson MiguelÁngel Sotelo David Fernández Llorca 

机构地区：[1]Computer Engineering Department,Polytechnic School,University of Alcala,Madrid,Spain [2]AI Sweden,Gothenburg,Sweden [3]Zenseact AB,Gothenburg,Sweden [4]Department of Electrical Engineering,Linköping University,Linköping,Sweden [5]Chalmers University of Technology,Gothenburg,Sweden [6]European Commission,Joint Research Centre,Seville,Spain

出　　处：《CAAI Transactions on Intelligence Technology》2024年第3期557-572,共16页智能技术学报（英文）

基　　金：European Commission,Joint Research Center,Grant/Award Number:HUMAINT;Ministerio de Ciencia e Innovación,Grant/Award Number:PID2020‐114924RB‐I00;Comunidad de Madrid,Grant/Award Number:S2018/EMT‐4362 SEGVAUTO 4.0‐CM。

摘　　要：Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.

关 键 词：autonomous vehicles EVALUATION INTERPRETABILITY multi-modal motion prediction ROBUSTNESS trustworthy AI 

分 类 号：U463.6[机械工程—车辆工程] TP391.41[交通运输工程—载运工具运用工程]