A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning

Di, Xuan; Shi, Rongye

doi:10.1016/j.trc.2021.103008

Cited by 145 publications

(61 citation statements)

References 180 publications

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“…Similar conclusions can be drawn from the reviews by Schwarting et al (43) and Yurtsever et al (44). To the best of the authors' knowledge, the only work that overviews learning-based AV control methods from artificial intelligence (AI) in the field of transportation engineering is Di and Shi (45). Nonetheless, that survey was focused primarily on how to deal with interactions between AVs and human-driven vehicles, especially by reference to academic works.…”

supporting

confidence: 59%

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Zhou

Laval

Zhou

et al. 2021

Transportation Research Record

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Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV industry has been mostly focusing on the long tail problem related to safety and has overlooked the impact on traffic congestion, (ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and (iii) although the reinforcement learning approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning, whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions for congestion mitigation for future mMP studies are proposed: (i) enrich data collection to facilitate the congestion learning, (ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and (iii) integrate domain knowledge from the traditional car-following theory to improve the string stability of mMP.

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supporting

confidence: 59%

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Zhou

Laval

Zhou

et al. 2021

Transportation Research Record

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“…The state measurement noise w(t) is a white noise satisfying |w(t)| ≤ 0.01. The initial vehicle state ( p i , v i ), i ∈ I [0,8] , are (160, 10), (140, 10), (120, 10), (100, 10), (80, 10), (60, 10), (40,10), (20,10) and (0, 10), respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…3 by applying Classic ACC, ADP, and Data-driven MPC to AV 5. Classic ACC is given in (10) with k h = 0.2, k v = 0.4, k s = 0.5, d still = 5 m, t g = 1.5 s and v set = 24.5 m/s, which are the default values in the MATLAB example "Adaptive Cruise Control with Sensor Fusion". ADP is designed based on the platoon model (7) and follows Algorithm 1 in [28] with Q = 10 −3 × I 3 and R = 1 but neglecting the driver reaction time.…”

Section: A Results Of Sub-platoonmentioning

confidence: 99%

“…and many CACC strategies have been developed for effective platooning of pure automated vehicles (AVs) [8], [9]. However, the penetration rate of AVs in the transportation system will remain unsaturated for a long time, resulting in the coexistence of AVs and human-driven vehicles (HVs) on roads [10]. Hence, it is in great need to develop CACC for mixed vehicle platoons consisting of both AVs and HVs.…”

mentioning

confidence: 99%

“…Robust control is designed in [24] for the AVs in large-scale mixed platoons. However, all the above control designs need to know the parameters of the OV model, which is too restrictive as the HV behaviours are difficult to be modelled exactly [10]. Moreover, both the OV model of HVs and the point-mass model of AVs adopted in the above works do not include the effect of time delays in propulsion, which could affect the platoon stability.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Robust Predictive Control for Mixed Vehicle Platoons Using Noisy Measurement

Lan

Zhao

2023

IEEE Trans. Intell. Transport. Syst.

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Some statistical challenges in automated driving systems

Caballero

Insua

Naveiro

2023

Appl Stoch Models Bus & Ind

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Automated driving systems are rapidly developing. However, numerous open problems remain to be resolved to ensure this technology progresses before its widespread adoption. A large subset of these problems are, or can be framed as, statistical decision problems. Therefore, we present herein several important statistical challenges that emerge when designing and operating automated driving systems. In particular, we focus on those that relate to request‐to‐intervene decisions, ethical decision support, operations in heterogeneous traffic, and algorithmic robustification. For each of these problems, earlier solution approaches are reviewed and alternative solutions are provided with accompanying empirical testing. We also highlight open avenues of inquiry for which applied statistical investigation can help ensure the maturation of automated driving systems. In so doing, we showcase the relevance of statistical research and practice within the context of this revolutionary technology.

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A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning

Cited by 145 publications

References 180 publications

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Data-Driven Robust Predictive Control for Mixed Vehicle Platoons Using Noisy Measurement

Some statistical challenges in automated driving systems

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